Trust in Artificial Intelligent Agents Scale
First quantitative approbation. Data analysis workflow
Anton Angelgardt
HSE University2021-05-15
Preprocess
Find preprocess workflow here.
Packages
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.4 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha## This is lavaan 0.6-8
## lavaan is FREE software! Please report any bugs.##
## Attaching package: 'lavaan'## The following object is masked from 'package:psych':
##
## cor2cov## corrplot 0.84 loadedImport data
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## id = col_character(),
## oth_text = col_character(),
## expoth_text = col_character(),
## sex = col_character(),
## edulvl1 = col_character(),
## spec1 = col_character(),
## edulvl2 = col_character(),
## spec2 = col_character(),
## jobfield = col_character(),
## jobpos = col_character(),
## city = col_character()
## )
## ℹ Use `spec()` for the full column specifications.## tibble [495 × 133] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ id : chr [1:495] "00XzIUUVmQ" "0aABrq9MBY" "0c6myGTrKr" "0CS5iaAVos" ...
## $ e_dighelp : num [1:495] 5 NA 4 3.33 3 ...
## $ n_dighelp : num [1:495] 1 NA 1 3 2 1 3 2 4 1 ...
## $ e_socnet : num [1:495] 0 NA 3.2 3.6 4.5 ...
## $ f_socnet : num [1:495] 3 NA 2.4 1.6 2 ...
## $ n_socnet : num [1:495] 2 NA 5 5 2 2 2 4 3 4 ...
## $ gt_score : num [1:495] 2.67 2.67 2.83 2.5 2.67 ...
## $ pr01 : num [1:495] 3 3 3 3 3 4 3 1 3 4 ...
## $ pr02 : num [1:495] 3 3 3 3 3 3 3 1 3 3 ...
## $ pr03 : num [1:495] 3 3 3 3 3 1 5 1 3 4 ...
## $ pr04 : num [1:495] 2 3 3 3 2 0 3 2 4 3 ...
## $ pr05 : num [1:495] 3 2 1 2 3 4 4 1 0 3 ...
## $ pr06 : num [1:495] 2 2 4 3 3 5 4 3 4 4 ...
## $ pr07 : num [1:495] 3 3 3 3 3 5 3 2 1 3 ...
## $ pr08 : num [1:495] 2 3 4 2 3 4 4 4 3 4 ...
## $ pr09 : num [1:495] 2 4 3 3 4 4 4 3 3 4 ...
## $ pr10 : num [1:495] 2 3 3 2 3 4 3 3 1 3 ...
## $ co01 : num [1:495] 2 3 3 2 3 4 3 2 4 3 ...
## $ co02 : num [1:495] 3 3 3 2 3 3 3 1 2 3 ...
## $ co03 : num [1:495] 3 4 4 2 3 4 4 2 2 3 ...
## $ co04 : num [1:495] 4 2 4 4 3 5 4 3 5 4 ...
## $ co05 : num [1:495] 2 2 3 2 3 4 4 2 3 2 ...
## $ co06 : num [1:495] 3 3 4 4 3 3 4 2 2 3 ...
## $ co07 : num [1:495] 2 3 1 1 1 1 1 3 0 1 ...
## $ co08 : num [1:495] 2 2 2 1 4 5 2 1 2 1 ...
## $ co09 : num [1:495] 2 3 2 1 4 4 3 2 1 2 ...
## $ co10 : num [1:495] 3 2 3 2 3 5 3 1 1 2 ...
## $ ut01 : num [1:495] 3 4 4 5 4 5 5 4 4 3 ...
## $ ut02 : num [1:495] 2 3 3 4 4 5 5 3 3 3 ...
## $ ut03 : num [1:495] 3 2 4 5 1 5 3 4 3 3 ...
## $ ut04 : num [1:495] 3 3 3 4 4 5 4 3 2 3 ...
## $ ut05 : num [1:495] 3 2 3 5 4 3 4 3 4 3 ...
## $ ut06 : num [1:495] 2 3 4 4 4 5 4 3 3 3 ...
## $ ut07 : num [1:495] 3 2 4 5 4 4 4 3 4 3 ...
## $ ut08 : num [1:495] 3 3 3 4 4 5 4 3 4 4 ...
## $ ut09 : num [1:495] 2 3 3 3 3 4 4 3 3 4 ...
## $ ut10 : num [1:495] 2 2 2 4 1 2 2 1 3 3 ...
## $ ut11 : num [1:495] 2 2 3 4 3 2 4 1 1 3 ...
## $ ut12 : num [1:495] 3 4 4 3 4 2 4 3 3 4 ...
## $ fa01 : num [1:495] 2 2 3 4 2 2 3 3 2 2 ...
## $ fa02 : num [1:495] 2 3 2 4 2 0 2 3 2 1 ...
## $ fa03 : num [1:495] 3 3 3 1 4 2 1 0 3 2 ...
## $ fa04 : num [1:495] 2 3 3 2 1 2 2 0 1 2 ...
## $ fa05 : num [1:495] 3 3 3 3 4 4 3 3 2 3 ...
## $ fa06 : num [1:495] 3 2 4 3 3 2 4 0 2 3 ...
## $ fa07 : num [1:495] 3 3 3 3 1 3 2 1 1 3 ...
## $ fa08 : num [1:495] 3 2 2 2 1 2 2 3 2 2 ...
## $ fa09 : num [1:495] 3 2 3 4 2 1 2 3 2 2 ...
## $ fa10 : num [1:495] 2 2 2 4 1 3 3 4 4 2 ...
## $ de01 : num [1:495] 3 2 3 3 3 3 4 3 3 3 ...
## $ de02 : num [1:495] 3 3 3 3 3 4 4 2 1 3 ...
## $ de03 : num [1:495] 3 3 4 3 3 5 3 2 1 3 ...
## $ de04 : num [1:495] 2 3 2 0 2 2 0 3 1 3 ...
## $ de05 : num [1:495] 3 3 4 3 3 5 5 4 4 4 ...
## $ de06 : num [1:495] 2 3 3 3 3 3 4 0 0 3 ...
## $ de07 : num [1:495] 3 2 3 3 3 3 4 3 3 3 ...
## $ de08 : num [1:495] 3 3 3 3 3 5 4 1 1 3 ...
## $ de09 : num [1:495] 4 2 3 3 3 5 5 4 1 4 ...
## $ de10 : num [1:495] 3 3 4 3 3 3 4 3 3 3 ...
## $ de11 : num [1:495] 2 3 2 3 2 1 4 4 1 1 ...
## $ un01 : num [1:495] 3 3 3 2 4 3 4 3 4 4 ...
## $ un02 : num [1:495] 2 2 3 2 4 3 4 2 3 3 ...
## $ un03 : num [1:495] 3 1 3 3 4 5 4 2 4 3 ...
## $ un04 : num [1:495] 3 1 3 3 4 3 3 2 4 3 ...
## $ un05 : num [1:495] 3 3 4 3 4 4 4 3 4 4 ...
## $ un06 : num [1:495] 3 3 2 1 1 1 4 1 4 4 ...
## $ un07 : num [1:495] 2 3 2 2 4 4 3 1 2 3 ...
## $ un08 : num [1:495] 2 3 3 3 4 4 4 4 4 3 ...
## $ un09 : num [1:495] 2 1 4 2 4 4 4 1 2 4 ...
## $ un10 : num [1:495] 3 3 3 2 4 3 4 2 2 4 ...
## $ un11 : num [1:495] 3 2 3 2 4 4 3 3 4 3 ...
## $ un12 : num [1:495] 3 2 3 3 4 4 4 3 4 3 ...
## $ gt01 : num [1:495] 3 3 3 2 3 2 1 1 1 2 ...
## $ gt02 : num [1:495] 3 2 3 3 3 3 1 1 1 2 ...
## $ gt03 : num [1:495] 3 3 3 3 3 2 1 1 1 3 ...
## $ gt04 : num [1:495] 3 2 3 2 3 4 1 1 2 2 ...
## $ gt05 : num [1:495] 2 3 2 2 1 4 0 2 0 2 ...
## $ gt06 : num [1:495] 2 3 3 3 3 3 3 1 1 3 ...
## $ socnet : num [1:495] 1 0 1 1 1 1 1 1 1 1 ...
## $ vk : num [1:495] 1 -2 1 1 1 1 1 1 1 1 ...
## $ fb : num [1:495] 0 -2 1 1 1 0 0 1 0 1 ...
## $ tw : num [1:495] 0 -2 1 0 0 0 0 0 0 0 ...
## $ in : num [1:495] 1 -2 1 1 0 1 1 1 1 1 ...
## $ tt : num [1:495] 0 -2 0 1 0 0 0 0 0 0 ...
## $ yt : num [1:495] 0 -2 1 1 0 0 0 1 1 1 ...
## $ freqvk : num [1:495] 3 -2 3 2 3 3 3 3 3 3 ...
## $ freqfb : num [1:495] -2 -2 2 2 1 -2 -2 0 -2 3 ...
## $ freqtw : num [1:495] -2 -2 2 -2 -2 -2 -2 -2 -2 -2 ...
## $ freqin : num [1:495] 3 -2 3 1 -2 3 3 2 3 3 ...
## $ freqtt : num [1:495] -2 -2 -2 1 -2 -2 -2 -2 -2 -2 ...
## $ freqyt : num [1:495] -2 -2 2 2 -2 -2 -2 2 2 3 ...
## $ expvk : num [1:495] 0 -2 4 3 5 3 5 4 3 3 ...
## $ expfb : num [1:495] -2 -2 3 3 4 -2 -2 2 -2 2 ...
## $ exptw : num [1:495] -2 -2 2 -2 -2 -2 -2 -2 -2 -2 ...
## $ expin : num [1:495] 0 -2 4 4 -2 4 5 2 2 4 ...
## $ exptt : num [1:495] -2 -2 -2 4 -2 -2 -2 -2 -2 -2 ...
## $ expyt : num [1:495] -2 -2 3 4 -2 -2 -2 4 2 3 ...
## $ dighelp : num [1:495] 1 0 1 1 1 1 1 1 1 1 ...
## $ siri : num [1:495] 0 -2 0 1 0 0 1 0 1 0 ...
## [list output truncated]
## - attr(*, "spec")=
## .. cols(
## .. id = col_character(),
## .. e_dighelp = col_double(),
## .. n_dighelp = col_double(),
## .. e_socnet = col_double(),
## .. f_socnet = col_double(),
## .. n_socnet = col_double(),
## .. gt_score = col_double(),
## .. pr01 = col_double(),
## .. pr02 = col_double(),
## .. pr03 = col_double(),
## .. pr04 = col_double(),
## .. pr05 = col_double(),
## .. pr06 = col_double(),
## .. pr07 = col_double(),
## .. pr08 = col_double(),
## .. pr09 = col_double(),
## .. pr10 = col_double(),
## .. co01 = col_double(),
## .. co02 = col_double(),
## .. co03 = col_double(),
## .. co04 = col_double(),
## .. co05 = col_double(),
## .. co06 = col_double(),
## .. co07 = col_double(),
## .. co08 = col_double(),
## .. co09 = col_double(),
## .. co10 = col_double(),
## .. ut01 = col_double(),
## .. ut02 = col_double(),
## .. ut03 = col_double(),
## .. ut04 = col_double(),
## .. ut05 = col_double(),
## .. ut06 = col_double(),
## .. ut07 = col_double(),
## .. ut08 = col_double(),
## .. ut09 = col_double(),
## .. ut10 = col_double(),
## .. ut11 = col_double(),
## .. ut12 = col_double(),
## .. fa01 = col_double(),
## .. fa02 = col_double(),
## .. fa03 = col_double(),
## .. fa04 = col_double(),
## .. fa05 = col_double(),
## .. fa06 = col_double(),
## .. fa07 = col_double(),
## .. fa08 = col_double(),
## .. fa09 = col_double(),
## .. fa10 = col_double(),
## .. de01 = col_double(),
## .. de02 = col_double(),
## .. de03 = col_double(),
## .. de04 = col_double(),
## .. de05 = col_double(),
## .. de06 = col_double(),
## .. de07 = col_double(),
## .. de08 = col_double(),
## .. de09 = col_double(),
## .. de10 = col_double(),
## .. de11 = col_double(),
## .. un01 = col_double(),
## .. un02 = col_double(),
## .. un03 = col_double(),
## .. un04 = col_double(),
## .. un05 = col_double(),
## .. un06 = col_double(),
## .. un07 = col_double(),
## .. un08 = col_double(),
## .. un09 = col_double(),
## .. un10 = col_double(),
## .. un11 = col_double(),
## .. un12 = col_double(),
## .. gt01 = col_double(),
## .. gt02 = col_double(),
## .. gt03 = col_double(),
## .. gt04 = col_double(),
## .. gt05 = col_double(),
## .. gt06 = col_double(),
## .. socnet = col_double(),
## .. vk = col_double(),
## .. fb = col_double(),
## .. tw = col_double(),
## .. `in` = col_double(),
## .. tt = col_double(),
## .. yt = col_double(),
## .. freqvk = col_double(),
## .. freqfb = col_double(),
## .. freqtw = col_double(),
## .. freqin = col_double(),
## .. freqtt = col_double(),
## .. freqyt = col_double(),
## .. expvk = col_double(),
## .. expfb = col_double(),
## .. exptw = col_double(),
## .. expin = col_double(),
## .. exptt = col_double(),
## .. expyt = col_double(),
## .. dighelp = col_double(),
## .. siri = col_double(),
## .. alice = col_double(),
## .. salut = col_double(),
## .. oleg = col_double(),
## .. alex = col_double(),
## .. mia = col_double(),
## .. mts = col_double(),
## .. ggle = col_double(),
## .. oth = col_double(),
## .. oth_text = col_character(),
## .. expsiri = col_double(),
## .. expalice = col_double(),
## .. expsalut = col_double(),
## .. expoleg = col_double(),
## .. expalex = col_double(),
## .. expmia = col_double(),
## .. expmts = col_double(),
## .. expggle = col_double(),
## .. expoth = col_double(),
## .. expoth_text = col_character(),
## .. selfdrcar = col_double(),
## .. selfdrexp = col_double(),
## .. selfdrsafe = col_double(),
## .. eduai = col_double(),
## .. eduaiexp = col_double(),
## .. age = col_double(),
## .. sex = col_character(),
## .. edulvl1 = col_character(),
## .. spec1 = col_character(),
## .. edu2 = col_double(),
## .. edulvl2 = col_character(),
## .. spec2 = col_character(),
## .. jobfield = col_character(),
## .. jobpos = col_character(),
## .. city = col_character()
## .. )Preparation
Vectors of TAIA items:
pr_items <- colnames(taia)[8:17]
co_items <- colnames(taia)[18:27]
ut_items <- colnames(taia)[28:39]
fa_items <- colnames(taia)[40:49]
de_items <- colnames(taia)[50:60]
un_items <- colnames(taia)[61:72]
taia_items <- colnames(taia)[8:72]Vector of GT items:
Column names for further formatting:
col_names <- c("", "Num. of obs.", "Mean", "SD",
"Median", "Trimmed Mean", "MAD",
"Min", "Max", "Range",
"Skewness", "Kurtuosis", "SE")
total_colnames <- c("Alpha", "Standardized Alpha", "Guttman's Lambda 6",
"Average interitem correlation", "S/N",
"Alpha SE", "Scale Mean", "Total Score SD",
"Median interitem correlation")
item_stats_colnames <- c("Num. of Obs.", "Discrimination",
"Std Cor",
"Cor Overlap Corrected",
"Cor if drop",
"Difficulty", "SD")
alpha_drop_colnames <- c("Alpha", "Standardized Alpha",
"Guttman's Lambda 6", "Average interitem correlation",
"S/N", "Alpha SE", "Var(r)","Median interitem correlation")Exploratory analysis
TAIA descriptive statistics
taia %>%
select(all_of(pr_items)) %>%
describe() %>%
kable(caption = "Predictability", label = 1, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| pr01 | 1 | 495 | 2.84 | 0.99 | 3 | 2.87 | 1.48 | 0 | 5 | 5 | -0.28 | 0.37 | 0.04 |
| pr02 | 2 | 495 | 2.73 | 0.97 | 3 | 2.77 | 1.48 | 0 | 5 | 5 | -0.19 | 0.10 | 0.04 |
| pr03 | 3 | 495 | 2.89 | 1.01 | 3 | 2.91 | 1.48 | 0 | 5 | 5 | -0.15 | -0.05 | 0.05 |
| pr04 | 4 | 495 | 2.84 | 1.04 | 3 | 2.87 | 1.48 | 0 | 5 | 5 | -0.18 | -0.04 | 0.05 |
| pr05 | 5 | 495 | 2.22 | 1.20 | 2 | 2.22 | 1.48 | 0 | 5 | 5 | 0.03 | -0.31 | 0.05 |
| pr06 | 6 | 495 | 3.04 | 1.07 | 3 | 3.06 | 1.48 | 0 | 5 | 5 | -0.26 | 0.07 | 0.05 |
| pr07 | 7 | 495 | 2.59 | 1.11 | 3 | 2.61 | 1.48 | 0 | 5 | 5 | -0.15 | -0.10 | 0.05 |
| pr08 | 8 | 495 | 3.05 | 0.91 | 3 | 3.09 | 0.00 | 0 | 5 | 5 | -0.56 | 1.26 | 0.04 |
| pr09 | 9 | 495 | 2.89 | 0.95 | 3 | 2.94 | 0.00 | 0 | 5 | 5 | -0.50 | 1.05 | 0.04 |
| pr10 | 10 | 495 | 2.83 | 1.04 | 3 | 2.90 | 1.48 | 0 | 5 | 5 | -0.41 | 0.28 | 0.05 |
taia %>% select(all_of(pr_items)) %>%
pivot_longer(cols = all_of(pr_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "darkred") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Predictability") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
pr01OK- Я считаю, что интеллектуальные системы надежны
pr02OK- Я считаю, что результаты работы интеллектуальных систем хорошо предсказуемы
pr03OK- Я считаю, что интеллектуальные системы ненадежны (R)
pr04OK- Я считаю, что результаты работы интеллектуальных систем невозможно предсказать (R)
pr05positive skewness- Я считаю, что поездки в машине, управляемой искусственным интеллектом, безопаснее, чем в обычной
pr06OK- Я считаю, что медицинская диагностика с применением интеллектуальных систем надеждее, чем без них
pr07OK- Я считаю, что финансовые операции под контролем искусственного интеллекта безопаснее, чем обычные
pr08high kurtosis- Я считаю, что интеллектуальные системы, обеспечивающие безопасность домов, хорошо справляются со своей задачей
pr09high kurtosis- Я считаю, что рекомендательные системы чаще всего правильно определяют предпочтения пользователей
pr10OK
taia %>%
select(all_of(co_items)) %>%
describe() %>%
kable(caption = "Consistency", label = 2, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| co01 | 1 | 495 | 2.49 | 1.08 | 3 | 2.51 | 1.48 | 0 | 5 | 5 | -0.17 | 0.13 | 0.05 |
| co02 | 2 | 495 | 2.51 | 1.04 | 3 | 2.53 | 1.48 | 0 | 5 | 5 | -0.19 | -0.06 | 0.05 |
| co03 | 3 | 495 | 2.86 | 1.02 | 3 | 2.92 | 1.48 | 0 | 5 | 5 | -0.40 | 0.31 | 0.05 |
| co04 | 4 | 495 | 3.47 | 1.09 | 4 | 3.54 | 1.48 | 0 | 5 | 5 | -0.57 | 0.32 | 0.05 |
| co05 | 5 | 495 | 2.20 | 1.11 | 2 | 2.17 | 1.48 | 0 | 5 | 5 | 0.10 | -0.17 | 0.05 |
| co06 | 6 | 495 | 2.52 | 1.11 | 3 | 2.53 | 1.48 | 0 | 5 | 5 | -0.13 | -0.15 | 0.05 |
| co07 | 7 | 495 | 1.59 | 1.13 | 2 | 1.52 | 1.48 | 0 | 5 | 5 | 0.54 | 0.12 | 0.05 |
| co08 | 8 | 495 | 1.90 | 1.04 | 2 | 1.86 | 1.48 | 0 | 5 | 5 | 0.40 | 0.28 | 0.05 |
| co09 | 9 | 495 | 2.05 | 1.07 | 2 | 2.01 | 1.48 | 0 | 5 | 5 | 0.35 | 0.12 | 0.05 |
| co10 | 10 | 495 | 2.44 | 1.10 | 2 | 2.44 | 1.48 | 0 | 5 | 5 | -0.04 | -0.06 | 0.05 |
taia %>% select(all_of(co_items)) %>%
pivot_longer(cols = all_of(co_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "chocolate3") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Consistency") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
co01OK- Интеллектуальные системы надежны, так как те системы, с которыми я сталкивался (сталкивалась), были надежными
co02OK- Я считаю, что если система при тестировании работает корректно, то и дальше она будет работать корректно
co03high kurtosis- Я считаю, что интеллектуальные системы совершают меньше технических ошибок, чем люди
co04high negative skewness- Со временем любая интеллектуальная система будет совершать всё меньше ошибок
co05positive skewness- Если интеллектуальные системы, с которыми я сталкивался (сталкивалась), были надежными, то я могу считать надёжными другие системы
co06positive skewness- Я могу судить о надёжности новой интеллектуальной системы по опыту работы с другими системами
co07high positive skewness- Я могу сказать, что система работает корректно, только протестировав её (R)
co08positive skewness- Если одна интеллектуальная система подводит меня, то и с другими будет то же самое
co09positive skewness- Если одна интеллектуальная система соответствует моим ожиданиям, то с другими будет то же самое
co10positive skewness- Я могу сформировать ожидания относительно работы интеллектуальных систем в целом на основе опыта взаимодействия с одной системой
taia %>%
select(all_of(ut_items)) %>%
describe() %>%
kable(caption = "Utility", label = 3, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ut01 | 1 | 495 | 3.78 | 1.05 | 4 | 3.88 | 1.48 | 0 | 5 | 5 | -0.86 | 1.12 | 0.05 |
| ut02 | 2 | 495 | 3.52 | 1.05 | 3 | 3.59 | 1.48 | 0 | 5 | 5 | -0.53 | 0.50 | 0.05 |
| ut03 | 3 | 495 | 3.56 | 1.11 | 4 | 3.64 | 1.48 | 0 | 5 | 5 | -0.57 | 0.15 | 0.05 |
| ut04 | 4 | 495 | 3.09 | 1.11 | 3 | 3.15 | 1.48 | 0 | 5 | 5 | -0.43 | 0.03 | 0.05 |
| ut05 | 5 | 495 | 3.05 | 1.21 | 3 | 3.09 | 1.48 | 0 | 5 | 5 | -0.33 | -0.15 | 0.05 |
| ut06 | 6 | 495 | 3.27 | 1.10 | 3 | 3.31 | 1.48 | 0 | 5 | 5 | -0.61 | 0.68 | 0.05 |
| ut07 | 7 | 495 | 3.20 | 1.13 | 3 | 3.23 | 1.48 | 0 | 5 | 5 | -0.28 | -0.22 | 0.05 |
| ut08 | 8 | 495 | 3.44 | 1.05 | 3 | 3.49 | 1.48 | 0 | 5 | 5 | -0.55 | 0.44 | 0.05 |
| ut09 | 9 | 495 | 3.18 | 1.17 | 3 | 3.23 | 1.48 | 0 | 5 | 5 | -0.47 | 0.16 | 0.05 |
| ut10 | 10 | 495 | 2.17 | 1.11 | 2 | 2.16 | 1.48 | 0 | 5 | 5 | 0.08 | -0.23 | 0.05 |
| ut11 | 11 | 495 | 2.67 | 1.23 | 3 | 2.69 | 1.48 | 0 | 5 | 5 | -0.11 | -0.41 | 0.06 |
| ut12 | 12 | 495 | 3.16 | 1.15 | 3 | 3.21 | 1.48 | 0 | 5 | 5 | -0.42 | 0.03 | 0.05 |
taia %>% select(all_of(ut_items)) %>%
pivot_longer(cols = all_of(ut_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "goldenrod3") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Utility") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
ut01extremely high negative skewness- Я считаю, что интеллектуальные технологии — неотъемлемая часть развития общества
ut02high negative skewness- Я считаю, что человечество нуждается в интеллектуальных системах
ut03high negative skewness- Мне кажется, человечеству будет лучше без интеллектуальных систем (R)
ut04negative skewness- Я считаю, что развитие интеллектуальных систем — это оправданный риск для общества
ut05OK- По моему мнению, без интеллектуальных систем общественный прогресс остановился бы
ut06negative skewness- Я считаю, что интеллектуальные системы существенно повышают качество медицинской диагностики
ut07light negative skewness- Мне кажется, что оформить услуги с цифровыми помощниками гораздо проще, чем без них
ut08negative skewness- Мне кажется, рекомендательные сервисы существенно сокращают время поиска нужной информации
ut09negative skewness- Я считаю, что если полиция будет использовать интеллектуальные системы, то количество раскрытых преступлений увеличится
ut10positive skewness- Мне кажется, при оформлении услуг через интернет можно справиться и без цифровых помощников (R)
ut11OK- Я считаю, что если на дорогах появятся машины, управляемые искусственным интеллектом, то число аварий снизится
ut12negative skewness- Я считаю, что использование интеллектуальных систем в обучении повышает качество образования
taia %>%
select(all_of(fa_items)) %>%
describe() %>%
kable(caption = "Faith", label = 4, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fa01 | 1 | 495 | 2.42 | 1.10 | 2 | 2.42 | 1.48 | 0 | 5 | 5 | -0.02 | -0.29 | 0.05 |
| fa02 | 2 | 495 | 2.16 | 1.18 | 2 | 2.15 | 1.48 | 0 | 5 | 5 | 0.18 | -0.42 | 0.05 |
| fa03 | 3 | 495 | 1.51 | 1.13 | 1 | 1.42 | 1.48 | 0 | 5 | 5 | 0.66 | 0.16 | 0.05 |
| fa04 | 4 | 495 | 1.57 | 1.08 | 1 | 1.51 | 1.48 | 0 | 5 | 5 | 0.55 | 0.17 | 0.05 |
| fa05 | 5 | 495 | 2.46 | 1.10 | 2 | 2.48 | 1.48 | 0 | 5 | 5 | -0.15 | -0.10 | 0.05 |
| fa06 | 6 | 495 | 2.47 | 1.08 | 3 | 2.49 | 1.48 | 0 | 5 | 5 | -0.18 | 0.06 | 0.05 |
| fa07 | 7 | 495 | 2.37 | 1.09 | 2 | 2.38 | 1.48 | 0 | 5 | 5 | -0.14 | -0.17 | 0.05 |
| fa08 | 8 | 495 | 2.21 | 1.14 | 2 | 2.17 | 1.48 | 0 | 5 | 5 | 0.28 | -0.13 | 0.05 |
| fa09 | 9 | 495 | 2.29 | 1.18 | 2 | 2.27 | 1.48 | 0 | 5 | 5 | 0.15 | -0.40 | 0.05 |
| fa10 | 10 | 495 | 2.64 | 1.20 | 3 | 2.62 | 1.48 | 0 | 5 | 5 | 0.06 | -0.28 | 0.05 |
taia %>% select(all_of(fa_items)) %>%
pivot_longer(cols = all_of(fa_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "darkgreen") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Faith") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
fa01OK- Я готов (готова) довериться интеллектуальным системам, даже если я не до конца понимаю, как они работают
fa02light positive skewness- Чтобы доверять интеллектуальной системе, мне нужно точно понимать, как работают её алгоритмы (R)
fa03hard positive skewness- Если интеллектуальная система перестает реагировать на запросы, мне с этим комфортно
fa04hard positive skewness- Если интеллектуальная система управлениями финансами не реагирует на запросы, я все равно буду уверен, что она работает корректно
fa05OK- Я готов (готова) доверять результатам работы интеллектуальных систем, даже если я не знаю, как они работают
fa06OK- Я готов (готова) довериться работе интеллектуальных систем так же, как работе профессионалов
fa07OK- Я предпочту сам (сама) контролировать весь процесс нежели дам контроль интеллектуальной системе (R)
fa08light positive skewness- Мне нужно знать детали работы алгоритма, чтобы быть уверенным (уверенной) в качестве результата его работы (R)
fa09OK- Если я не понимаю, как работает интеллектуальная система, я не могу быть уверенным (уверенной) в результате её работы (R)
fa10OK- Мне не важно, как работает интеллектуальная система, если она должным образом справляется со своей задачей
taia %>%
select(all_of(de_items)) %>%
describe() %>%
kable(caption = "Dependability", label = 5, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| de01 | 1 | 495 | 2.59 | 1.10 | 3 | 2.64 | 1.48 | 0 | 5 | 5 | -0.42 | 0.08 | 0.05 |
| de02 | 2 | 495 | 2.17 | 1.15 | 2 | 2.18 | 1.48 | 0 | 5 | 5 | 0.00 | -0.34 | 0.05 |
| de03 | 3 | 495 | 2.17 | 1.19 | 2 | 2.17 | 1.48 | 0 | 5 | 5 | 0.04 | -0.30 | 0.05 |
| de04 | 4 | 495 | 1.90 | 1.05 | 2 | 1.85 | 1.48 | 0 | 5 | 5 | 0.55 | 0.66 | 0.05 |
| de05 | 5 | 495 | 3.57 | 1.16 | 4 | 3.68 | 1.48 | 0 | 5 | 5 | -0.78 | 0.48 | 0.05 |
| de06 | 6 | 495 | 2.23 | 1.23 | 2 | 2.25 | 1.48 | 0 | 5 | 5 | 0.01 | -0.43 | 0.06 |
| de07 | 7 | 495 | 2.82 | 1.00 | 3 | 2.86 | 1.48 | 0 | 5 | 5 | -0.30 | 0.31 | 0.04 |
| de08 | 8 | 495 | 2.65 | 1.06 | 3 | 2.70 | 1.48 | 0 | 5 | 5 | -0.40 | 0.14 | 0.05 |
| de09 | 9 | 495 | 3.44 | 1.20 | 4 | 3.54 | 1.48 | 0 | 5 | 5 | -0.61 | -0.14 | 0.05 |
| de10 | 10 | 495 | 2.25 | 1.18 | 2 | 2.28 | 1.48 | 0 | 5 | 5 | -0.22 | -0.42 | 0.05 |
| de11 | 11 | 495 | 2.31 | 1.20 | 2 | 2.31 | 1.48 | 0 | 5 | 5 | 0.00 | -0.52 | 0.05 |
taia %>% select(all_of(de_items)) %>%
pivot_longer(cols = all_of(de_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "darkblue") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Dependability") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
de01negative skewness- Я готов (готова) следовать рекомендациям рекомендательных систем социальных сетей
de02OK- Я готов (готова) делегировать управление финансами интеллектуальному помощнику
de03OK- Если интеллектуальная система запрашивает мои личные данные, я могу быть уверен (уверенна) в их сохранности
de04hard positive skewness- Я могу доверять интеллектуальной системе, если я точно понимаю, какие опасности исходят от неё
de05hard negative skewness- Я бы мог (могла) жить в «умном доме»
de06negative kurtosis- Если бы я ехал (ехала) в автомобиле, управляемом искусственным интеллектом, я бы был спокоен за свою безопасность
de07OK- Если меня обследуют с помощью интеллектуальных медицинских технологий, я могу доверять выставленному диагнозу
de08OK- Я считаю, что при покупках с интеллектуальными помощниками риск ошибиться меньше, чем без них
de09negative skewness- Мне было бы некомфортно жить в «умном доме» (R)
de10negative kurtosis- Я могу доверить искусственному интеллекту задачи, от которых зависит моя личная безопасность
de11negative kurtosis- Я могу доверить искусственному интеллекту только рутинные задачи (например, уборка) (R)
taia %>%
select(all_of(un_items)) %>%
describe() %>%
kable(caption = "Understanding", label = 6, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| un01 | 1 | 495 | 2.93 | 1.05 | 3 | 3.01 | 1.48 | 0 | 5 | 5 | -0.48 | 0.31 | 0.05 |
| un02 | 2 | 495 | 2.47 | 1.14 | 3 | 2.49 | 1.48 | 0 | 5 | 5 | -0.17 | -0.27 | 0.05 |
| un03 | 3 | 495 | 3.02 | 1.17 | 3 | 3.10 | 1.48 | 0 | 5 | 5 | -0.55 | 0.01 | 0.05 |
| un04 | 4 | 495 | 2.61 | 1.09 | 3 | 2.65 | 1.48 | 0 | 5 | 5 | -0.33 | -0.22 | 0.05 |
| un05 | 5 | 495 | 2.82 | 1.10 | 3 | 2.90 | 1.48 | 0 | 5 | 5 | -0.51 | 0.24 | 0.05 |
| un06 | 6 | 495 | 2.29 | 1.23 | 2 | 2.26 | 1.48 | 0 | 5 | 5 | 0.19 | -0.62 | 0.06 |
| un07 | 7 | 495 | 2.13 | 1.18 | 2 | 2.14 | 1.48 | 0 | 5 | 5 | -0.02 | -0.54 | 0.05 |
| un08 | 8 | 495 | 2.90 | 1.16 | 3 | 2.96 | 1.48 | 0 | 5 | 5 | -0.45 | 0.00 | 0.05 |
| un09 | 9 | 495 | 2.32 | 1.23 | 2 | 2.37 | 1.48 | 0 | 5 | 5 | -0.18 | -0.75 | 0.06 |
| un10 | 10 | 495 | 2.24 | 1.15 | 2 | 2.23 | 1.48 | 0 | 5 | 5 | 0.05 | -0.44 | 0.05 |
| un11 | 11 | 495 | 2.63 | 1.20 | 3 | 2.66 | 1.48 | 0 | 5 | 5 | -0.25 | -0.37 | 0.05 |
| un12 | 12 | 495 | 2.89 | 1.12 | 3 | 2.96 | 1.48 | 0 | 5 | 5 | -0.46 | 0.13 | 0.05 |
taia %>% select(all_of(un_items)) %>%
pivot_longer(cols = all_of(un_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "purple4") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Understanding") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
un01OK- Я понимаю, как происходит взаимодействие человека с интеллектуальными системами
un02OK- Я понимаю, как работают алгоритмы интеллектуальных систем
un03negative skewness- Я стараюсь разобраться в том, как работают интеллектуальные системы
un04negative skewness- Я понимаю, как работают отдельные элементы интеллектуальных систем
un05positive kurtosis- Я понимаю общий принцип работы интеллектуальных систем
un06negative kurtosis- Я плохо разбираюсь в тонкостях работы интеллектуальных систем (R)
un07negative kurtosis- Мне понятно, как работают интеллектуальные алгоритмы, применяемые в медицинской диагностике
un08OK- Мне понятно, как работают алгоритмы рекомендательных систем социальных сетей
un09extra negative kurtosis- Я понимаю, как работают алгоритмы автомобилей, управляемых искусственным интеллектом
un10negative kurtosis- Я понимаю, как работают алгоритмы интеллектуальных систем, которые управляют финансами
un11OK- Мне понятно, как устроены алгоритмы машинного перевода текстов
un12OK- Мне понятно, как работают алгоритмы систем типа «умный дом»
Correlations
Psychometric Analysis
Subscales
Predictability
pr1 <- psych::alpha(
taia %>% select(all_of(pr_items)),
cumulative = TRUE,
title = "Predictability Factor",
check.keys = FALSE
)kable(pr1$total,
caption = "Perdictability. Subscale statistics",
label = 7, digits = 2,
col.names = total_colnames
)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.81 | 0.81 | 0.82 | 0.3 | 4.25 | 0.01 | 27.92 | 6.23 | 0.33 |
pr1$item.stats$mean <- pr1$item.stats$mean / 5
kable(pr1$item.stats,
caption = "Predictability. Items statistics",
label = 8, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| pr01 | 495 | 0.79 | 0.80 | 0.80 | 0.72 | 0.57 | 0.99 |
| pr02 | 495 | 0.66 | 0.66 | 0.62 | 0.56 | 0.55 | 0.97 |
| pr03 | 495 | 0.45 | 0.45 | 0.37 | 0.31 | 0.58 | 1.01 |
| pr04 | 495 | 0.32 | 0.32 | 0.21 | 0.16 | 0.57 | 1.04 |
| pr05 | 495 | 0.61 | 0.59 | 0.51 | 0.46 | 0.44 | 1.20 |
| pr06 | 495 | 0.62 | 0.62 | 0.56 | 0.50 | 0.61 | 1.07 |
| pr07 | 495 | 0.74 | 0.73 | 0.70 | 0.63 | 0.52 | 1.11 |
| pr08 | 495 | 0.72 | 0.73 | 0.71 | 0.64 | 0.61 | 0.91 |
| pr09 | 495 | 0.61 | 0.62 | 0.56 | 0.50 | 0.58 | 0.95 |
| pr10 | 495 | 0.55 | 0.56 | 0.47 | 0.42 | 0.57 | 1.04 |
pr1$item.stats %>%
ggplot(aes(x = row.names(pr1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Predictability. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))
kable(pr1$alpha.drop,
caption = "Predictability. Subscale statistics when item drop",
label = 9, digits = 2, col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| pr01 | 0.76 | 0.77 | 0.78 | 0.27 | 3.27 | 0.02 | 0.02 | 0.29 |
| pr02 | 0.78 | 0.79 | 0.80 | 0.29 | 3.66 | 0.01 | 0.03 | 0.32 |
| pr03 | 0.81 | 0.81 | 0.81 | 0.32 | 4.33 | 0.01 | 0.03 | 0.36 |
| pr04 | 0.82 | 0.83 | 0.82 | 0.35 | 4.78 | 0.01 | 0.02 | 0.36 |
| pr05 | 0.79 | 0.80 | 0.81 | 0.30 | 3.88 | 0.01 | 0.03 | 0.33 |
| pr06 | 0.79 | 0.79 | 0.81 | 0.30 | 3.79 | 0.01 | 0.03 | 0.33 |
| pr07 | 0.77 | 0.78 | 0.79 | 0.28 | 3.46 | 0.02 | 0.02 | 0.30 |
| pr08 | 0.77 | 0.78 | 0.79 | 0.28 | 3.46 | 0.02 | 0.02 | 0.30 |
| pr09 | 0.79 | 0.79 | 0.80 | 0.30 | 3.80 | 0.01 | 0.03 | 0.35 |
| pr10 | 0.80 | 0.80 | 0.81 | 0.31 | 3.99 | 0.01 | 0.03 | 0.35 |
kable(pr1$response.freq,
caption = "Predictability. Non missing response frequency for each item",
label = 10, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| pr01 | 0.02 | 0.06 | 0.24 | 0.45 | 0.19 | 0.04 | 0 |
| pr02 | 0.01 | 0.08 | 0.28 | 0.43 | 0.17 | 0.03 | 0 |
| pr03 | 0.01 | 0.06 | 0.26 | 0.40 | 0.22 | 0.05 | 0 |
| pr04 | 0.02 | 0.07 | 0.27 | 0.38 | 0.21 | 0.05 | 0 |
| pr05 | 0.09 | 0.17 | 0.33 | 0.29 | 0.09 | 0.03 | 0 |
| pr06 | 0.02 | 0.06 | 0.20 | 0.41 | 0.23 | 0.08 | 0 |
| pr07 | 0.04 | 0.12 | 0.28 | 0.38 | 0.14 | 0.04 | 0 |
| pr08 | 0.02 | 0.03 | 0.16 | 0.52 | 0.24 | 0.04 | 0 |
| pr09 | 0.02 | 0.05 | 0.18 | 0.53 | 0.17 | 0.04 | 0 |
| pr10 | 0.02 | 0.08 | 0.21 | 0.45 | 0.20 | 0.04 | 0 |
Consistency
co1 <- psych::alpha(
taia %>% select(all_of(co_items)),
cumulative = TRUE,
title = "Consistency Factor",
check.keys = FALSE
)## Warning in psych::alpha(taia %>% select(all_of(co_items)), cumulative = TRUE, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( co07 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' optionkable(co1$total,
caption = "Consistency. Subscale statistics",
label = 11, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.76 | 0.77 | 0.8 | 0.25 | 3.27 | 0.01 | 24.03 | 6.1 | 0.27 |
co1$item.stats$mean <- co1$item.stats$mean / 5
kable(co1$item.stats,
caption = "Consistency. Items statistics",
label = 12, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| co01 | 495 | 0.74 | 0.74 | 0.72 | 0.64 | 0.50 | 1.08 |
| co02 | 495 | 0.68 | 0.69 | 0.65 | 0.57 | 0.50 | 1.04 |
| co03 | 495 | 0.55 | 0.55 | 0.48 | 0.41 | 0.57 | 1.02 |
| co04 | 495 | 0.40 | 0.41 | 0.30 | 0.24 | 0.69 | 1.09 |
| co05 | 495 | 0.79 | 0.78 | 0.79 | 0.70 | 0.44 | 1.11 |
| co06 | 495 | 0.65 | 0.65 | 0.61 | 0.53 | 0.50 | 1.11 |
| co07 | 495 | -0.03 | -0.04 | -0.22 | -0.22 | 0.32 | 1.13 |
| co08 | 495 | 0.45 | 0.46 | 0.36 | 0.30 | 0.38 | 1.04 |
| co09 | 495 | 0.76 | 0.77 | 0.76 | 0.67 | 0.41 | 1.07 |
| co10 | 495 | 0.67 | 0.67 | 0.62 | 0.56 | 0.49 | 1.10 |
co1$item.stats %>%
ggplot(aes(x = row.names(co1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Consistency. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))
kable(co1$alpha.drop,
caption = "Consistency. Subscale statistics when item drop",
label = 13, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| co01 | 0.71 | 0.72 | 0.76 | 0.22 | 2.52 | 0.02 | 0.06 | 0.26 |
| co02 | 0.72 | 0.73 | 0.77 | 0.23 | 2.66 | 0.02 | 0.06 | 0.26 |
| co03 | 0.74 | 0.75 | 0.79 | 0.25 | 2.98 | 0.02 | 0.07 | 0.27 |
| co04 | 0.77 | 0.77 | 0.80 | 0.27 | 3.36 | 0.01 | 0.06 | 0.35 |
| co05 | 0.70 | 0.71 | 0.75 | 0.21 | 2.42 | 0.02 | 0.06 | 0.26 |
| co06 | 0.73 | 0.73 | 0.77 | 0.23 | 2.74 | 0.02 | 0.06 | 0.27 |
| co07 | 0.83 | 0.82 | 0.83 | 0.34 | 4.70 | 0.01 | 0.03 | 0.36 |
| co08 | 0.76 | 0.76 | 0.79 | 0.26 | 3.23 | 0.02 | 0.07 | 0.34 |
| co09 | 0.71 | 0.71 | 0.75 | 0.22 | 2.47 | 0.02 | 0.06 | 0.26 |
| co10 | 0.72 | 0.73 | 0.77 | 0.23 | 2.69 | 0.02 | 0.07 | 0.26 |
kable(co1$response.freq,
caption = "Consistency. Non missing response frequency for each item",
label = 14, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| co01 | 0.05 | 0.11 | 0.31 | 0.39 | 0.11 | 0.03 | 0 |
| co02 | 0.03 | 0.12 | 0.32 | 0.38 | 0.13 | 0.02 | 0 |
| co03 | 0.02 | 0.07 | 0.22 | 0.44 | 0.21 | 0.04 | 0 |
| co04 | 0.01 | 0.04 | 0.10 | 0.35 | 0.32 | 0.18 | 0 |
| co05 | 0.06 | 0.19 | 0.37 | 0.27 | 0.09 | 0.02 | 0 |
| co06 | 0.04 | 0.14 | 0.28 | 0.38 | 0.13 | 0.03 | 0 |
| co07 | 0.18 | 0.31 | 0.32 | 0.14 | 0.04 | 0.02 | 0 |
| co08 | 0.08 | 0.27 | 0.42 | 0.16 | 0.06 | 0.01 | 0 |
| co09 | 0.06 | 0.24 | 0.40 | 0.22 | 0.06 | 0.02 | 0 |
| co10 | 0.04 | 0.14 | 0.33 | 0.34 | 0.11 | 0.03 | 0 |
Utility
ut1 <- psych::alpha(
taia %>% select(all_of(ut_items)),
cumulative = TRUE,
title = "Utility Factor",
check.keys = FALSE
)kable(ut1$total,
caption = "Utility. Subscale statistics",
label = 15, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.86 | 0.86 | 0.87 | 0.34 | 6.17 | 0.01 | 38.09 | 8.44 | 0.37 |
ut1$item.stats$mean <- ut1$item.stats$mean / 5
kable(ut1$item.stats,
caption = "Utility. Items statistics",
label = 16, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| ut01 | 495 | 0.77 | 0.78 | 0.78 | 0.71 | 0.76 | 1.05 |
| ut02 | 495 | 0.82 | 0.83 | 0.84 | 0.77 | 0.70 | 1.05 |
| ut03 | 495 | 0.57 | 0.57 | 0.52 | 0.47 | 0.71 | 1.11 |
| ut04 | 495 | 0.51 | 0.51 | 0.44 | 0.40 | 0.62 | 1.11 |
| ut05 | 495 | 0.69 | 0.69 | 0.65 | 0.61 | 0.61 | 1.21 |
| ut06 | 495 | 0.76 | 0.76 | 0.74 | 0.70 | 0.65 | 1.10 |
| ut07 | 495 | 0.63 | 0.63 | 0.59 | 0.54 | 0.64 | 1.13 |
| ut08 | 495 | 0.65 | 0.66 | 0.62 | 0.57 | 0.69 | 1.05 |
| ut09 | 495 | 0.68 | 0.68 | 0.64 | 0.59 | 0.64 | 1.17 |
| ut10 | 495 | 0.17 | 0.17 | 0.06 | 0.04 | 0.43 | 1.11 |
| ut11 | 495 | 0.56 | 0.55 | 0.48 | 0.45 | 0.53 | 1.23 |
| ut12 | 495 | 0.71 | 0.71 | 0.68 | 0.64 | 0.63 | 1.15 |
ut1$item.stats %>%
ggplot(aes(x = row.names(ut1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Utility. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))
kable(ut1$alpha.drop,
caption = "Utility. Subscale statistics when item drop",
label = 17, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| ut01 | 0.84 | 0.84 | 0.85 | 0.32 | 5.16 | 0.01 | 0.03 | 0.34 |
| ut02 | 0.83 | 0.83 | 0.84 | 0.31 | 5.00 | 0.01 | 0.03 | 0.33 |
| ut03 | 0.85 | 0.85 | 0.86 | 0.35 | 5.84 | 0.01 | 0.04 | 0.41 |
| ut04 | 0.86 | 0.86 | 0.87 | 0.36 | 6.06 | 0.01 | 0.03 | 0.41 |
| ut05 | 0.84 | 0.84 | 0.86 | 0.33 | 5.45 | 0.01 | 0.04 | 0.37 |
| ut06 | 0.84 | 0.84 | 0.85 | 0.32 | 5.21 | 0.01 | 0.03 | 0.33 |
| ut07 | 0.85 | 0.85 | 0.86 | 0.34 | 5.65 | 0.01 | 0.04 | 0.37 |
| ut08 | 0.85 | 0.85 | 0.86 | 0.34 | 5.55 | 0.01 | 0.03 | 0.37 |
| ut09 | 0.84 | 0.85 | 0.86 | 0.33 | 5.49 | 0.01 | 0.03 | 0.37 |
| ut10 | 0.88 | 0.88 | 0.88 | 0.40 | 7.40 | 0.01 | 0.01 | 0.41 |
| ut11 | 0.85 | 0.86 | 0.87 | 0.35 | 5.92 | 0.01 | 0.04 | 0.41 |
| ut12 | 0.84 | 0.84 | 0.86 | 0.33 | 5.37 | 0.01 | 0.03 | 0.35 |
kable(ut1$response.freq,
caption = "Utility. Non missing response frequency for each item",
label = 18, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| ut01 | 0.01 | 0.01 | 0.06 | 0.29 | 0.34 | 0.28 | 0 |
| ut02 | 0.01 | 0.02 | 0.09 | 0.38 | 0.30 | 0.20 | 0 |
| ut03 | 0.01 | 0.04 | 0.09 | 0.33 | 0.31 | 0.22 | 0 |
| ut04 | 0.02 | 0.08 | 0.15 | 0.40 | 0.27 | 0.09 | 0 |
| ut05 | 0.03 | 0.06 | 0.20 | 0.35 | 0.23 | 0.12 | 0 |
| ut06 | 0.03 | 0.03 | 0.13 | 0.39 | 0.30 | 0.12 | 0 |
| ut07 | 0.01 | 0.05 | 0.19 | 0.35 | 0.26 | 0.14 | 0 |
| ut08 | 0.01 | 0.03 | 0.11 | 0.36 | 0.34 | 0.15 | 0 |
| ut09 | 0.03 | 0.05 | 0.15 | 0.38 | 0.25 | 0.13 | 0 |
| ut10 | 0.07 | 0.19 | 0.37 | 0.26 | 0.09 | 0.02 | 0 |
| ut11 | 0.05 | 0.12 | 0.27 | 0.32 | 0.18 | 0.07 | 0 |
| ut12 | 0.02 | 0.07 | 0.15 | 0.38 | 0.26 | 0.12 | 0 |
Faith
fa1 <- psych::alpha(
taia %>% select(all_of(fa_items)),
cumulative = TRUE,
title = "Faith Factor",
check.keys = FALSE
)kable(fa1$total,
caption = "Faith. Subscale statistics",
label = 19, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.77 | 0.77 | 0.81 | 0.25 | 3.26 | 0.02 | 22.1 | 6.41 | 0.24 |
fa1$item.stats$mean <- fa1$item.stats$mean / 5
kable(fa1$item.stats,
caption = "Faith. Items statistics",
label = 20, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| fa01 | 495 | 0.76 | 0.76 | 0.76 | 0.67 | 0.48 | 1.10 |
| fa02 | 495 | 0.61 | 0.60 | 0.56 | 0.47 | 0.43 | 1.18 |
| fa03 | 495 | 0.27 | 0.28 | 0.16 | 0.10 | 0.30 | 1.13 |
| fa04 | 495 | 0.41 | 0.42 | 0.33 | 0.26 | 0.31 | 1.08 |
| fa05 | 495 | 0.77 | 0.78 | 0.78 | 0.69 | 0.49 | 1.10 |
| fa06 | 495 | 0.55 | 0.56 | 0.50 | 0.42 | 0.49 | 1.08 |
| fa07 | 495 | 0.42 | 0.42 | 0.31 | 0.26 | 0.47 | 1.09 |
| fa08 | 495 | 0.58 | 0.57 | 0.52 | 0.44 | 0.44 | 1.14 |
| fa09 | 495 | 0.66 | 0.65 | 0.62 | 0.53 | 0.46 | 1.18 |
| fa10 | 495 | 0.63 | 0.62 | 0.57 | 0.50 | 0.53 | 1.20 |
fa1$item.stats %>%
ggplot(aes(x = row.names(fa1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Faith. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))
kable(fa1$alpha.drop,
caption = "Faith. Subscale statistics when item drop",
label = 21, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| fa01 | 0.71 | 0.71 | 0.76 | 0.22 | 2.47 | 0.02 | 0.04 | 0.22 |
| fa02 | 0.74 | 0.74 | 0.78 | 0.24 | 2.87 | 0.02 | 0.04 | 0.24 |
| fa03 | 0.79 | 0.79 | 0.82 | 0.29 | 3.70 | 0.01 | 0.04 | 0.30 |
| fa04 | 0.77 | 0.77 | 0.81 | 0.27 | 3.30 | 0.02 | 0.04 | 0.26 |
| fa05 | 0.71 | 0.71 | 0.76 | 0.21 | 2.43 | 0.02 | 0.04 | 0.22 |
| fa06 | 0.75 | 0.75 | 0.79 | 0.25 | 2.95 | 0.02 | 0.05 | 0.29 |
| fa07 | 0.77 | 0.77 | 0.81 | 0.27 | 3.30 | 0.02 | 0.05 | 0.30 |
| fa08 | 0.74 | 0.75 | 0.79 | 0.25 | 2.92 | 0.02 | 0.04 | 0.24 |
| fa09 | 0.73 | 0.73 | 0.78 | 0.23 | 2.73 | 0.02 | 0.04 | 0.24 |
| fa10 | 0.74 | 0.74 | 0.79 | 0.24 | 2.79 | 0.02 | 0.04 | 0.24 |
kable(fa1$response.freq,
caption = "Faith. Non missing response frequency for each item",
label = 22, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| fa01 | 0.04 | 0.16 | 0.32 | 0.33 | 0.13 | 0.03 | 0 |
| fa02 | 0.08 | 0.21 | 0.36 | 0.21 | 0.12 | 0.02 | 0 |
| fa03 | 0.19 | 0.35 | 0.29 | 0.11 | 0.05 | 0.01 | 0 |
| fa04 | 0.16 | 0.34 | 0.33 | 0.12 | 0.04 | 0.01 | 0 |
| fa05 | 0.05 | 0.12 | 0.33 | 0.34 | 0.13 | 0.03 | 0 |
| fa06 | 0.05 | 0.12 | 0.32 | 0.38 | 0.11 | 0.03 | 0 |
| fa07 | 0.05 | 0.15 | 0.32 | 0.35 | 0.11 | 0.02 | 0 |
| fa08 | 0.05 | 0.20 | 0.38 | 0.23 | 0.10 | 0.03 | 0 |
| fa09 | 0.06 | 0.19 | 0.34 | 0.25 | 0.13 | 0.03 | 0 |
| fa10 | 0.04 | 0.12 | 0.31 | 0.32 | 0.14 | 0.08 | 0 |
Dependability
de1 <- psych::alpha(
taia %>% select(all_of(de_items)),
cumulative = TRUE,
title = "Dependability Factor",
check.keys = FALSE
)## Warning in psych::alpha(taia %>% select(all_of(de_items)), cumulative = TRUE, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( de04 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' optionkable(de1$total,
caption = "Dependability. Subscale statistics",
label = 23, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.75 | 0.75 | 0.8 | 0.21 | 2.96 | 0.02 | 28.09 | 6.74 | 0.24 |
de1$item.stats$mean <- de1$item.stats$mean / 5
kable(de1$item.stats,
caption = "Dependability. Items statistics",
label = 24, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| de01 | 495 | 0.57 | 0.58 | 0.52 | 0.45 | 0.52 | 1.10 |
| de02 | 495 | 0.72 | 0.72 | 0.69 | 0.61 | 0.43 | 1.15 |
| de03 | 495 | 0.66 | 0.66 | 0.61 | 0.54 | 0.43 | 1.19 |
| de04 | 495 | -0.23 | -0.22 | -0.39 | -0.36 | 0.38 | 1.05 |
| de05 | 495 | 0.59 | 0.58 | 0.56 | 0.46 | 0.71 | 1.16 |
| de06 | 495 | 0.67 | 0.67 | 0.62 | 0.55 | 0.45 | 1.23 |
| de07 | 495 | 0.58 | 0.60 | 0.54 | 0.47 | 0.56 | 1.00 |
| de08 | 495 | 0.67 | 0.68 | 0.66 | 0.57 | 0.53 | 1.06 |
| de09 | 495 | 0.45 | 0.44 | 0.39 | 0.30 | 0.69 | 1.20 |
| de10 | 495 | 0.74 | 0.74 | 0.72 | 0.64 | 0.45 | 1.18 |
| de11 | 495 | 0.43 | 0.42 | 0.30 | 0.27 | 0.46 | 1.20 |
de1$item.stats %>%
ggplot(aes(x = row.names(de1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Dependability. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))
kable(de1$alpha.drop,
caption = "Dependability. Subscale statistics when item drop",
label = 25, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| de01 | 0.73 | 0.72 | 0.78 | 0.21 | 2.59 | 0.02 | 0.07 | 0.22 |
| de02 | 0.71 | 0.70 | 0.76 | 0.19 | 2.32 | 0.02 | 0.07 | 0.22 |
| de03 | 0.72 | 0.71 | 0.77 | 0.20 | 2.44 | 0.02 | 0.07 | 0.23 |
| de04 | 0.82 | 0.82 | 0.84 | 0.31 | 4.49 | 0.01 | 0.02 | 0.32 |
| de05 | 0.73 | 0.72 | 0.76 | 0.21 | 2.59 | 0.02 | 0.07 | 0.22 |
| de06 | 0.71 | 0.71 | 0.77 | 0.19 | 2.42 | 0.02 | 0.07 | 0.22 |
| de07 | 0.73 | 0.72 | 0.78 | 0.20 | 2.56 | 0.02 | 0.07 | 0.22 |
| de08 | 0.71 | 0.70 | 0.76 | 0.19 | 2.39 | 0.02 | 0.06 | 0.22 |
| de09 | 0.75 | 0.74 | 0.78 | 0.22 | 2.88 | 0.02 | 0.07 | 0.30 |
| de10 | 0.70 | 0.69 | 0.76 | 0.19 | 2.28 | 0.02 | 0.06 | 0.22 |
| de11 | 0.75 | 0.75 | 0.80 | 0.23 | 2.93 | 0.02 | 0.08 | 0.32 |
kable(de1$response.freq,
caption = "Dependability. Non missing response frequency for each item",
label = 26, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| de01 | 0.05 | 0.11 | 0.24 | 0.43 | 0.14 | 0.03 | 0 |
| de02 | 0.09 | 0.17 | 0.36 | 0.26 | 0.10 | 0.02 | 0 |
| de03 | 0.10 | 0.17 | 0.35 | 0.27 | 0.09 | 0.03 | 0 |
| de04 | 0.07 | 0.26 | 0.45 | 0.14 | 0.05 | 0.02 | 0 |
| de05 | 0.02 | 0.03 | 0.10 | 0.28 | 0.34 | 0.23 | 0 |
| de06 | 0.10 | 0.17 | 0.32 | 0.28 | 0.11 | 0.03 | 0 |
| de07 | 0.02 | 0.06 | 0.27 | 0.42 | 0.20 | 0.03 | 0 |
| de08 | 0.04 | 0.10 | 0.24 | 0.44 | 0.15 | 0.03 | 0 |
| de09 | 0.01 | 0.06 | 0.13 | 0.27 | 0.33 | 0.20 | 0 |
| de10 | 0.10 | 0.15 | 0.30 | 0.34 | 0.10 | 0.02 | 0 |
| de11 | 0.07 | 0.20 | 0.28 | 0.31 | 0.12 | 0.03 | 0 |
Understanding
un1 <- psych::alpha(
taia %>% select(all_of(un_items)),
cumulative = TRUE,
title = "Understanding Factor",
check.keys = FALSE
)kable(un1$total,
caption = "Understanding. Subscale statistics",
label = 27, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.92 | 0.92 | 0.92 | 0.5 | 12 | 0.01 | 31.24 | 10.15 | 0.51 |
un1$item.stats$mean <- un1$item.stats$mean / 5
kable(un1$item.stats,
caption = "Understanding. Items statistics",
label = 28, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| un01 | 495 | 0.75 | 0.75 | 0.73 | 0.70 | 0.59 | 1.05 |
| un02 | 495 | 0.84 | 0.84 | 0.84 | 0.81 | 0.49 | 1.14 |
| un03 | 495 | 0.59 | 0.59 | 0.54 | 0.51 | 0.60 | 1.17 |
| un04 | 495 | 0.76 | 0.76 | 0.73 | 0.71 | 0.52 | 1.09 |
| un05 | 495 | 0.81 | 0.81 | 0.80 | 0.77 | 0.56 | 1.10 |
| un06 | 495 | 0.57 | 0.56 | 0.50 | 0.48 | 0.46 | 1.23 |
| un07 | 495 | 0.72 | 0.72 | 0.69 | 0.66 | 0.43 | 1.18 |
| un08 | 495 | 0.76 | 0.76 | 0.75 | 0.71 | 0.58 | 1.16 |
| un09 | 495 | 0.73 | 0.73 | 0.69 | 0.67 | 0.46 | 1.23 |
| un10 | 495 | 0.75 | 0.75 | 0.72 | 0.69 | 0.45 | 1.15 |
| un11 | 495 | 0.79 | 0.79 | 0.77 | 0.74 | 0.53 | 1.20 |
| un12 | 495 | 0.75 | 0.76 | 0.73 | 0.70 | 0.58 | 1.12 |
un1$item.stats %>%
ggplot(aes(x = row.names(un1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Understanding. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))
kable(un1$alpha.drop,
caption = "Understanding. Subscale statistics when item drop",
label = 29, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| un01 | 0.91 | 0.92 | 0.91 | 0.50 | 10.87 | 0.01 | 0.01 | 0.51 |
| un02 | 0.91 | 0.91 | 0.91 | 0.48 | 10.26 | 0.01 | 0.01 | 0.50 |
| un03 | 0.92 | 0.92 | 0.92 | 0.52 | 12.05 | 0.01 | 0.01 | 0.54 |
| un04 | 0.91 | 0.92 | 0.92 | 0.50 | 10.82 | 0.01 | 0.01 | 0.51 |
| un05 | 0.91 | 0.91 | 0.91 | 0.49 | 10.46 | 0.01 | 0.01 | 0.50 |
| un06 | 0.92 | 0.92 | 0.92 | 0.53 | 12.30 | 0.01 | 0.01 | 0.54 |
| un07 | 0.92 | 0.92 | 0.92 | 0.50 | 11.10 | 0.01 | 0.01 | 0.51 |
| un08 | 0.91 | 0.92 | 0.91 | 0.50 | 10.80 | 0.01 | 0.01 | 0.51 |
| un09 | 0.92 | 0.92 | 0.92 | 0.50 | 11.06 | 0.01 | 0.01 | 0.51 |
| un10 | 0.91 | 0.92 | 0.92 | 0.50 | 10.93 | 0.01 | 0.01 | 0.51 |
| un11 | 0.91 | 0.91 | 0.91 | 0.49 | 10.62 | 0.01 | 0.01 | 0.50 |
| un12 | 0.91 | 0.92 | 0.92 | 0.50 | 10.86 | 0.01 | 0.01 | 0.50 |
kable(un1$response.freq,
caption = "Understanding. Non missing response frequency for each item",
label = 30, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| un01 | 0.02 | 0.07 | 0.19 | 0.43 | 0.24 | 0.05 | 0 |
| un02 | 0.05 | 0.14 | 0.29 | 0.35 | 0.14 | 0.03 | 0 |
| un03 | 0.03 | 0.08 | 0.16 | 0.36 | 0.29 | 0.07 | 0 |
| un04 | 0.03 | 0.13 | 0.24 | 0.40 | 0.17 | 0.02 | 0 |
| un05 | 0.04 | 0.08 | 0.19 | 0.44 | 0.21 | 0.04 | 0 |
| un06 | 0.06 | 0.24 | 0.28 | 0.25 | 0.14 | 0.04 | 0 |
| un07 | 0.09 | 0.21 | 0.30 | 0.29 | 0.09 | 0.02 | 0 |
| un08 | 0.04 | 0.09 | 0.18 | 0.39 | 0.23 | 0.06 | 0 |
| un09 | 0.08 | 0.18 | 0.25 | 0.30 | 0.17 | 0.01 | 0 |
| un10 | 0.06 | 0.20 | 0.33 | 0.27 | 0.12 | 0.02 | 0 |
| un11 | 0.05 | 0.13 | 0.23 | 0.36 | 0.18 | 0.05 | 0 |
| un12 | 0.03 | 0.08 | 0.19 | 0.41 | 0.23 | 0.06 | 0 |
omega(taia %>% ungroup() %>% select(all_of(taia_items)),
nfactors=6, p=.05, poly=FALSE,
digits=2, title="Omega", sl=TRUE, plot=TRUE, covar=FALSE)## Loading required namespace: GPArotation
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.94
## G.6: 0.97
## Omega Hierarchical: 0.6
## Omega H asymptotic: 0.62
## Omega Total 0.96
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* F4* F5* F6* h2 u2 p2
## pr01 0.57 0.34 0.26 0.57 0.43 0.58
## pr02 0.45 0.32 0.68 0.65
## pr03 0.20 0.54 0.39 0.61 0.10
## pr04 0.43 0.24 0.76 0.02
## pr05 0.56 0.45 0.52 0.48 0.60
## pr06 0.43 0.37 0.35 0.65 0.53
## pr07 0.57 0.24 0.25 0.21 0.50 0.50 0.66
## pr08 0.52 0.38 0.23 0.49 0.51 0.54
## pr09 0.41 0.29 0.29 0.71 0.57
## pr10 0.37 0.25 0.23 0.77 0.58
## co01 0.51 0.51 0.54 0.46 0.48
## co02 0.44 0.55 0.49 0.51 0.39
## co03 0.47 0.29 0.37 0.63 0.60
## co04 0.33 0.44 0.38 0.62 0.28
## co05 0.44 0.63 0.60 0.40 0.33
## co06 0.37 0.51 0.40 0.60 0.34
## co07- 0.29 0.17 0.83 0.11
## co08 0.34 -0.37 0.41 0.59 0.02
## co09 0.40 0.60 0.54 0.46 0.29
## co10 0.38 0.45 0.40 0.60 0.36
## ut01 0.36 0.67 0.61 0.39 0.21
## ut02 0.45 0.63 0.64 0.36 0.31
## ut03 0.22 0.32 0.52 0.54 0.46 0.09
## ut04 0.27 0.39 0.24 0.76 0.31
## ut05 0.41 0.44 0.37 0.63 0.45
## ut06 0.46 0.60 0.56 0.44 0.38
## ut07 0.34 0.46 0.34 0.66 0.35
## ut08 0.40 0.47 0.40 0.60 0.39
## ut09 0.42 0.46 0.39 0.61 0.44
## ut10 0.24 0.09 0.91 0.03
## ut11 0.54 0.21 0.39 0.48 0.52 0.61
## ut12 0.46 0.47 0.45 0.55 0.47
## fa01 0.44 0.61 0.60 0.40 0.33
## fa02 0.67 0.54 0.46 0.00
## fa03 -0.37 0.31 0.69 0.12
## fa04 0.35 0.32 0.21 0.40 0.60 0.32
## fa05 0.47 0.60 0.62 0.38 0.36
## fa06 0.58 0.30 0.52 0.48 0.65
## fa07 0.23 0.20 0.47 0.36 0.64 0.14
## fa08 0.61 0.49 0.51 0.00
## fa09 0.64 0.23 0.55 0.45 0.02
## fa10 0.24 0.63 0.48 0.52 0.12
## de01 0.43 0.29 0.71 0.63
## de02 0.57 0.23 0.30 0.49 0.51 0.67
## de03 0.50 0.28 0.38 0.62 0.65
## de04- 0.31 0.39 0.27 0.73 0.37
## de05 0.38 0.41 0.29 0.45 0.55 0.32
## de06 0.58 0.47 0.58 0.42 0.59
## de07 0.49 0.34 0.40 0.60 0.59
## de08 0.54 0.23 0.25 0.43 0.57 0.68
## de09 0.22 0.59 0.43 0.57 0.12
## de10 0.60 0.40 0.56 0.44 0.64
## de11 0.22 0.24 0.32 0.27 0.73 0.18
## un01 0.25 0.72 0.63 0.37 0.10
## un02 0.28 0.79 0.70 0.30 0.11
## un03 0.43 -0.26 0.36 0.64 0.10
## un04 0.25 0.68 0.53 0.47 0.12
## un05 0.28 0.77 0.67 0.33 0.11
## un06 -0.29 0.51 0.32 0.40 0.60 0.02
## un07 0.33 0.59 0.54 0.46 0.20
## un08 0.24 0.73 0.60 0.40 0.10
## un09 0.31 0.61 0.52 0.48 0.18
## un10 0.27 0.65 0.57 0.43 0.13
## un11 0.24 0.72 0.60 0.40 0.10
## un12 0.26 0.68 0.57 0.43 0.12
##
## With eigenvalues of:
## g F1* F2* F3* F4* F5* F6*
## 9.4 4.6 5.6 2.8 2.7 1.6 2.3
##
## general/max 1.68 max/min = 3.6
## mean percent general = 0.32 with sd = 0.22 and cv of 0.68
## Explained Common Variance of the general factor = 0.32
##
## The degrees of freedom are 1705 and the fit is 8.03
## The number of observations was 495 with Chi Square = 3753.55 with prob < 2.4e-155
## The root mean square of the residuals is 0.04
## The df corrected root mean square of the residuals is 0.04
## RMSEA index = 0.049 and the 10 % confidence intervals are 0.047 0.051
## BIC = -6825.22
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 2015 and the fit is 22.88
## The number of observations was 495 with Chi Square = 10774.78 with prob < 0
## The root mean square of the residuals is 0.15
## The df corrected root mean square of the residuals is 0.15
##
## RMSEA index = 0.094 and the 10 % confidence intervals are 0.092 0.096
## BIC = -1727.41
##
## Measures of factor score adequacy
## g F1* F2* F3* F4* F5*
## Correlation of scores with factors 0.81 0.89 0.97 0.86 0.94 0.71
## Multiple R square of scores with factors 0.66 0.79 0.94 0.74 0.88 0.50
## Minimum correlation of factor score estimates 0.32 0.59 0.88 0.47 0.76 0.00
## F6*
## Correlation of scores with factors 0.91
## Multiple R square of scores with factors 0.83
## Minimum correlation of factor score estimates 0.66
##
## Total, General and Subset omega for each subset
## g F1* F2* F3* F4* F5*
## Omega total for total scores and subscales 0.96 0.87 0.91 0.86 0.83 0.81
## Omega general for total scores and subscales 0.60 0.43 0.11 0.45 0.09 0.57
## Omega group for total scores and subscales 0.21 0.44 0.79 0.41 0.74 0.24
## F6*
## Omega total for total scores and subscales 0.51
## Omega general for total scores and subscales 0.14
## Omega group for total scores and subscales 0.37splitHalf(taia %>% ungroup() %>% select(all_of(taia_items)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")## Split half reliabilities
## Call: splitHalf(r = taia %>% ungroup() %>% select(all_of(taia_items)),
## raw = F, brute = F, n.sample = 100, covar = F, check.keys = F,
## key = NULL, use = "pairwise")
##
## Maximum split half reliability (lambda 4) = 0.96
## Guttman lambda 6 = 0.96
## Average split half reliability = 0.93
## Guttman lambda 3 (alpha) = 0.93
## Guttman lambda 2 = 0.94
## Minimum split half reliability (beta) = 0.86
## Average interitem r = 0.17 with median = 0.18## Warning: Guttman has been deprecated. The use of the splitHalf function is
## recommended## Warning in splitHalf(r): Some items were negatively correlated with total scale
## and were automatically reversed.## Call: guttman(r = taia %>% ungroup() %>% select(all_of(taia_items)))
##
## Alternative estimates of reliability
##
## Guttman bounds
## L1 = 0.92
## L2 = 0.94
## L3 (alpha) = 0.93
## L4 (max) = 0.97
## L5 = 0.92
## L6 (smc) = 0.96
## TenBerge bounds
## mu0 = 0.93 mu1 = 0.94 mu2 = 0.94 mu3 = 0.94
##
## alpha of first PC = 0.95
## estimated greatest lower bound based upon communalities= 0.97
##
## beta found by splitHalf = 0.85## $glb
## [1] 0.965817
##
## $communality
## pr01 pr02 pr03 pr04 pr05 pr06 pr07 pr08
## 0.7253391 0.5393523 0.4690491 0.6292470 0.7830601 0.6280428 0.5758103 0.6112839
## pr09 pr10 co01 co02 co03 co04 co05 co06
## 0.5896570 0.3330770 0.5758243 0.6305638 0.4895706 0.4890165 0.7521395 0.5726406
## co07 co08 co09 co10 ut01 ut02 ut03 ut04
## 0.4356987 0.4665793 0.6337356 0.4811587 0.7718201 0.7277348 0.6422830 0.3722353
## ut05 ut06 ut07 ut08 ut09 ut10 ut11 ut12
## 0.5234489 0.7171434 0.5757501 0.5792722 0.7253062 0.3644927 0.6451266 0.5389414
## fa01 fa02 fa03 fa04 fa05 fa06 fa07 fa08
## 0.6943597 0.5928412 0.4376811 0.5197275 0.7565165 0.5844505 0.4786126 0.6061162
## fa09 fa10 de01 de02 de03 de04 de05 de06
## 0.6945327 0.4919291 0.4216417 0.5711187 0.5654506 0.4521109 0.7942688 0.7264322
## de07 de08 de09 de10 de11 un01 un02 un03
## 0.5278337 0.5885239 0.6875419 0.6514868 0.3425354 0.6718202 0.7379031 0.4402195
## un04 un05 un06 un07 un08 un09 un10 un11
## 0.6398019 0.6914535 0.4729219 0.5759967 0.6904648 0.5762576 0.6507091 0.6870705
## un12
## 0.6276157
##
## $numf
## [1] 19
##
## $Call
## glb.fa(r = taia %>% ungroup() %>% select(all_of(taia_items)))Items exclusion
First step
Excluded items: co07, ut10, de04
Reason: negative discrimination
Stems:
co07Я могу сказать, что система работает корректно, только протестировав её (R)ut10Мне кажется, при оформлении услуг через интернет можно справиться и без цифровых помощников (R)de04Я могу доверять интеллектуальной системе, если я точно понимаю, какие опасности исходят от неё (R)
Subscales after first step of exclusion
Consistency
co2 <- psych::alpha(
taia %>% select(all_of(co_items)),
cumulative = TRUE,
title = "Consistency Factor",
check.keys = FALSE
)kable(co2$total,
caption = "Consistency. Subscale statistics",
label = 11, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.83 | 0.82 | 0.83 | 0.34 | 4.7 | 0.01 | 22.44 | 6.24 | 0.36 |
co2$item.stats$mean <- co2$item.stats$mean / 5
kable(co2$item.stats,
caption = "Consistency. Items statistics",
label = 12, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| co01 | 495 | 0.74 | 0.74 | 0.71 | 0.65 | 0.50 | 1.08 |
| co02 | 495 | 0.72 | 0.72 | 0.67 | 0.62 | 0.50 | 1.04 |
| co03 | 495 | 0.56 | 0.57 | 0.48 | 0.43 | 0.57 | 1.02 |
| co04 | 495 | 0.44 | 0.43 | 0.33 | 0.28 | 0.69 | 1.09 |
| co05 | 495 | 0.79 | 0.78 | 0.78 | 0.70 | 0.44 | 1.11 |
| co06 | 495 | 0.68 | 0.67 | 0.62 | 0.56 | 0.50 | 1.11 |
| co08 | 495 | 0.44 | 0.44 | 0.34 | 0.29 | 0.38 | 1.04 |
| co09 | 495 | 0.76 | 0.76 | 0.75 | 0.67 | 0.41 | 1.07 |
| co10 | 495 | 0.68 | 0.68 | 0.62 | 0.57 | 0.49 | 1.10 |
co2$item.stats %>%
ggplot(aes(x = row.names(co2$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Consistency. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))
kable(co2$alpha.drop,
caption = "Consistency. Subscale statistics when item drop",
label = 13, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| co01 | 0.79 | 0.79 | 0.80 | 0.32 | 3.81 | 0.01 | 0.03 | 0.34 |
| co02 | 0.80 | 0.80 | 0.81 | 0.33 | 3.89 | 0.01 | 0.03 | 0.35 |
| co03 | 0.82 | 0.82 | 0.82 | 0.36 | 4.49 | 0.01 | 0.03 | 0.41 |
| co04 | 0.84 | 0.83 | 0.83 | 0.39 | 5.04 | 0.01 | 0.02 | 0.41 |
| co05 | 0.79 | 0.79 | 0.79 | 0.31 | 3.66 | 0.01 | 0.02 | 0.33 |
| co06 | 0.80 | 0.80 | 0.81 | 0.34 | 4.06 | 0.01 | 0.03 | 0.35 |
| co08 | 0.83 | 0.83 | 0.83 | 0.39 | 5.01 | 0.01 | 0.02 | 0.40 |
| co09 | 0.79 | 0.79 | 0.80 | 0.32 | 3.74 | 0.01 | 0.02 | 0.34 |
| co10 | 0.80 | 0.80 | 0.81 | 0.34 | 4.04 | 0.01 | 0.03 | 0.35 |
kable(co2$response.freq,
caption = "Consistency. Non missing response frequency for each item",
label = 14, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| co01 | 0.05 | 0.11 | 0.31 | 0.39 | 0.11 | 0.03 | 0 |
| co02 | 0.03 | 0.12 | 0.32 | 0.38 | 0.13 | 0.02 | 0 |
| co03 | 0.02 | 0.07 | 0.22 | 0.44 | 0.21 | 0.04 | 0 |
| co04 | 0.01 | 0.04 | 0.10 | 0.35 | 0.32 | 0.18 | 0 |
| co05 | 0.06 | 0.19 | 0.37 | 0.27 | 0.09 | 0.02 | 0 |
| co06 | 0.04 | 0.14 | 0.28 | 0.38 | 0.13 | 0.03 | 0 |
| co08 | 0.08 | 0.27 | 0.42 | 0.16 | 0.06 | 0.01 | 0 |
| co09 | 0.06 | 0.24 | 0.40 | 0.22 | 0.06 | 0.02 | 0 |
| co10 | 0.04 | 0.14 | 0.33 | 0.34 | 0.11 | 0.03 | 0 |
Utility
ut2 <- psych::alpha(
taia %>% select(all_of(ut_items)),
cumulative = TRUE,
title = "Utility Factor",
check.keys = FALSE
)kable(ut2$total,
caption = "Utility. Subscale statistics",
label = 15, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.88 | 0.88 | 0.88 | 0.4 | 7.4 | 0.01 | 35.92 | 8.32 | 0.41 |
ut2$item.stats$mean <- ut2$item.stats$mean / 5
kable(ut2$item.stats,
caption = "Utility. Items statistics",
label = 16, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| ut01 | 495 | 0.79 | 0.79 | 0.79 | 0.73 | 0.76 | 1.05 |
| ut02 | 495 | 0.83 | 0.84 | 0.84 | 0.79 | 0.70 | 1.05 |
| ut03 | 495 | 0.55 | 0.56 | 0.49 | 0.45 | 0.71 | 1.11 |
| ut04 | 495 | 0.53 | 0.53 | 0.46 | 0.42 | 0.62 | 1.11 |
| ut05 | 495 | 0.69 | 0.68 | 0.64 | 0.60 | 0.61 | 1.21 |
| ut06 | 495 | 0.77 | 0.77 | 0.75 | 0.71 | 0.65 | 1.10 |
| ut07 | 495 | 0.62 | 0.62 | 0.57 | 0.52 | 0.64 | 1.13 |
| ut08 | 495 | 0.66 | 0.67 | 0.62 | 0.58 | 0.69 | 1.05 |
| ut09 | 495 | 0.70 | 0.70 | 0.66 | 0.62 | 0.64 | 1.17 |
| ut11 | 495 | 0.57 | 0.56 | 0.49 | 0.46 | 0.53 | 1.23 |
| ut12 | 495 | 0.72 | 0.72 | 0.68 | 0.65 | 0.63 | 1.15 |
ut2$item.stats %>%
ggplot(aes(x = row.names(ut2$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Utility. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))
kable(ut2$alpha.drop,
caption = "Utility. Subscale statistics when item drop",
label = 17, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| ut01 | 0.86 | 0.86 | 0.86 | 0.38 | 6.21 | 0.01 | 0.01 | 0.40 |
| ut02 | 0.86 | 0.86 | 0.86 | 0.38 | 6.01 | 0.01 | 0.01 | 0.37 |
| ut03 | 0.88 | 0.88 | 0.88 | 0.42 | 7.30 | 0.01 | 0.01 | 0.43 |
| ut04 | 0.88 | 0.88 | 0.88 | 0.43 | 7.41 | 0.01 | 0.01 | 0.43 |
| ut05 | 0.87 | 0.87 | 0.87 | 0.40 | 6.69 | 0.01 | 0.01 | 0.41 |
| ut06 | 0.86 | 0.86 | 0.87 | 0.39 | 6.30 | 0.01 | 0.01 | 0.41 |
| ut07 | 0.87 | 0.87 | 0.87 | 0.41 | 6.99 | 0.01 | 0.01 | 0.42 |
| ut08 | 0.87 | 0.87 | 0.87 | 0.40 | 6.77 | 0.01 | 0.01 | 0.41 |
| ut09 | 0.87 | 0.87 | 0.87 | 0.40 | 6.64 | 0.01 | 0.02 | 0.41 |
| ut11 | 0.88 | 0.88 | 0.88 | 0.42 | 7.27 | 0.01 | 0.01 | 0.44 |
| ut12 | 0.86 | 0.87 | 0.87 | 0.39 | 6.52 | 0.01 | 0.02 | 0.41 |
kable(ut2$response.freq,
caption = "Utility. Non missing response frequency for each item",
label = 18, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| ut01 | 0.01 | 0.01 | 0.06 | 0.29 | 0.34 | 0.28 | 0 |
| ut02 | 0.01 | 0.02 | 0.09 | 0.38 | 0.30 | 0.20 | 0 |
| ut03 | 0.01 | 0.04 | 0.09 | 0.33 | 0.31 | 0.22 | 0 |
| ut04 | 0.02 | 0.08 | 0.15 | 0.40 | 0.27 | 0.09 | 0 |
| ut05 | 0.03 | 0.06 | 0.20 | 0.35 | 0.23 | 0.12 | 0 |
| ut06 | 0.03 | 0.03 | 0.13 | 0.39 | 0.30 | 0.12 | 0 |
| ut07 | 0.01 | 0.05 | 0.19 | 0.35 | 0.26 | 0.14 | 0 |
| ut08 | 0.01 | 0.03 | 0.11 | 0.36 | 0.34 | 0.15 | 0 |
| ut09 | 0.03 | 0.05 | 0.15 | 0.38 | 0.25 | 0.13 | 0 |
| ut11 | 0.05 | 0.12 | 0.27 | 0.32 | 0.18 | 0.07 | 0 |
| ut12 | 0.02 | 0.07 | 0.15 | 0.38 | 0.26 | 0.12 | 0 |
Dependability
de2 <- psych::alpha(
taia %>% select(all_of(de_items)),
cumulative = TRUE,
title = "Dependability Factor",
check.keys = FALSE
)kable(de2$total,
caption = "Dependability. Subscale statistics",
label = 23, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.82 | 0.82 | 0.84 | 0.31 | 4.49 | 0.01 | 26.19 | 7.05 | 0.32 |
de2$item.stats$mean <- de2$item.stats$mean / 5
kable(de2$item.stats,
caption = "Dependability. Items statistics",
label = 24, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| de01 | 495 | 0.58 | 0.59 | 0.53 | 0.47 | 0.52 | 1.10 |
| de02 | 495 | 0.72 | 0.72 | 0.68 | 0.62 | 0.43 | 1.15 |
| de03 | 495 | 0.65 | 0.65 | 0.60 | 0.54 | 0.43 | 1.19 |
| de05 | 495 | 0.62 | 0.62 | 0.59 | 0.50 | 0.71 | 1.16 |
| de06 | 495 | 0.67 | 0.67 | 0.62 | 0.56 | 0.45 | 1.23 |
| de07 | 495 | 0.61 | 0.62 | 0.56 | 0.51 | 0.56 | 1.00 |
| de08 | 495 | 0.70 | 0.71 | 0.67 | 0.61 | 0.53 | 1.06 |
| de09 | 495 | 0.46 | 0.45 | 0.40 | 0.31 | 0.69 | 1.20 |
| de10 | 495 | 0.73 | 0.73 | 0.71 | 0.64 | 0.45 | 1.18 |
| de11 | 495 | 0.41 | 0.40 | 0.28 | 0.25 | 0.46 | 1.20 |
de2$item.stats %>%
ggplot(aes(x = row.names(de2$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Dependability. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))
kable(de2$alpha.drop,
caption = "Dependability. Subscale statistics when item drop",
label = 25, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| de01 | 0.80 | 0.80 | 0.82 | 0.31 | 4.11 | 0.01 | 0.02 | 0.31 |
| de02 | 0.79 | 0.79 | 0.81 | 0.29 | 3.72 | 0.01 | 0.02 | 0.29 |
| de03 | 0.79 | 0.80 | 0.82 | 0.30 | 3.93 | 0.01 | 0.02 | 0.29 |
| de05 | 0.80 | 0.80 | 0.80 | 0.31 | 4.04 | 0.01 | 0.02 | 0.35 |
| de06 | 0.79 | 0.80 | 0.81 | 0.30 | 3.88 | 0.01 | 0.02 | 0.31 |
| de07 | 0.80 | 0.80 | 0.82 | 0.31 | 4.02 | 0.01 | 0.02 | 0.29 |
| de08 | 0.79 | 0.79 | 0.81 | 0.29 | 3.75 | 0.01 | 0.02 | 0.29 |
| de09 | 0.82 | 0.82 | 0.82 | 0.34 | 4.60 | 0.01 | 0.02 | 0.35 |
| de10 | 0.78 | 0.79 | 0.80 | 0.29 | 3.67 | 0.01 | 0.02 | 0.29 |
| de11 | 0.83 | 0.83 | 0.84 | 0.35 | 4.79 | 0.01 | 0.02 | 0.37 |
kable(de2$response.freq,
caption = "Dependability. Non missing response frequency for each item",
label = 26, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| de01 | 0.05 | 0.11 | 0.24 | 0.43 | 0.14 | 0.03 | 0 |
| de02 | 0.09 | 0.17 | 0.36 | 0.26 | 0.10 | 0.02 | 0 |
| de03 | 0.10 | 0.17 | 0.35 | 0.27 | 0.09 | 0.03 | 0 |
| de05 | 0.02 | 0.03 | 0.10 | 0.28 | 0.34 | 0.23 | 0 |
| de06 | 0.10 | 0.17 | 0.32 | 0.28 | 0.11 | 0.03 | 0 |
| de07 | 0.02 | 0.06 | 0.27 | 0.42 | 0.20 | 0.03 | 0 |
| de08 | 0.04 | 0.10 | 0.24 | 0.44 | 0.15 | 0.03 | 0 |
| de09 | 0.01 | 0.06 | 0.13 | 0.27 | 0.33 | 0.20 | 0 |
| de10 | 0.10 | 0.15 | 0.30 | 0.34 | 0.10 | 0.02 | 0 |
| de11 | 0.07 | 0.20 | 0.28 | 0.31 | 0.12 | 0.03 | 0 |
Reliability measures
omega(taia %>% ungroup() %>% select(all_of(taia_items)),
nfactors=6, p=.05, poly=FALSE,
digits=2, title="Omega", sl=TRUE, plot=TRUE, covar=FALSE)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.94
## G.6: 0.97
## Omega Hierarchical: 0.6
## Omega H asymptotic: 0.62
## Omega Total 0.96
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* F4* F5* F6* h2 u2 p2
## pr01 0.57 0.34 0.26 0.57 0.43 0.58
## pr02 0.45 0.32 0.68 0.65
## pr03 0.20 0.54 0.39 0.61 0.10
## pr04 0.43 0.24 0.76 0.02
## pr05 0.56 0.45 0.52 0.48 0.60
## pr06 0.43 0.37 0.35 0.65 0.53
## pr07 0.57 0.24 0.25 0.21 0.50 0.50 0.66
## pr08 0.52 0.38 0.23 0.49 0.51 0.54
## pr09 0.41 0.29 0.29 0.71 0.57
## pr10 0.37 0.25 0.23 0.77 0.58
## co01 0.51 0.51 0.54 0.46 0.48
## co02 0.44 0.55 0.49 0.51 0.39
## co03 0.47 0.29 0.37 0.63 0.60
## co04 0.33 0.44 0.38 0.62 0.28
## co05 0.44 0.63 0.60 0.40 0.33
## co06 0.37 0.51 0.40 0.60 0.34
## co07- 0.29 0.17 0.83 0.11
## co08 0.34 -0.37 0.41 0.59 0.02
## co09 0.40 0.60 0.54 0.46 0.29
## co10 0.38 0.45 0.40 0.60 0.36
## ut01 0.36 0.67 0.61 0.39 0.21
## ut02 0.45 0.63 0.64 0.36 0.31
## ut03 0.22 0.32 0.52 0.54 0.46 0.09
## ut04 0.27 0.39 0.24 0.76 0.31
## ut05 0.41 0.44 0.37 0.63 0.45
## ut06 0.46 0.60 0.56 0.44 0.38
## ut07 0.34 0.46 0.34 0.66 0.35
## ut08 0.40 0.47 0.40 0.60 0.39
## ut09 0.42 0.46 0.39 0.61 0.44
## ut10 0.24 0.09 0.91 0.03
## ut11 0.54 0.21 0.39 0.48 0.52 0.61
## ut12 0.46 0.47 0.45 0.55 0.47
## fa01 0.44 0.61 0.60 0.40 0.33
## fa02 0.67 0.54 0.46 0.00
## fa03 -0.37 0.31 0.69 0.12
## fa04 0.35 0.32 0.21 0.40 0.60 0.32
## fa05 0.47 0.60 0.62 0.38 0.36
## fa06 0.58 0.30 0.52 0.48 0.65
## fa07 0.23 0.20 0.47 0.36 0.64 0.14
## fa08 0.61 0.49 0.51 0.00
## fa09 0.64 0.23 0.55 0.45 0.02
## fa10 0.24 0.63 0.48 0.52 0.12
## de01 0.43 0.29 0.71 0.63
## de02 0.57 0.23 0.30 0.49 0.51 0.67
## de03 0.50 0.28 0.38 0.62 0.65
## de04- 0.31 0.39 0.27 0.73 0.37
## de05 0.38 0.41 0.29 0.45 0.55 0.32
## de06 0.58 0.47 0.58 0.42 0.59
## de07 0.49 0.34 0.40 0.60 0.59
## de08 0.54 0.23 0.25 0.43 0.57 0.68
## de09 0.22 0.59 0.43 0.57 0.12
## de10 0.60 0.40 0.56 0.44 0.64
## de11 0.22 0.24 0.32 0.27 0.73 0.18
## un01 0.25 0.72 0.63 0.37 0.10
## un02 0.28 0.79 0.70 0.30 0.11
## un03 0.43 -0.26 0.36 0.64 0.10
## un04 0.25 0.68 0.53 0.47 0.12
## un05 0.28 0.77 0.67 0.33 0.11
## un06 -0.29 0.51 0.32 0.40 0.60 0.02
## un07 0.33 0.59 0.54 0.46 0.20
## un08 0.24 0.73 0.60 0.40 0.10
## un09 0.31 0.61 0.52 0.48 0.18
## un10 0.27 0.65 0.57 0.43 0.13
## un11 0.24 0.72 0.60 0.40 0.10
## un12 0.26 0.68 0.57 0.43 0.12
##
## With eigenvalues of:
## g F1* F2* F3* F4* F5* F6*
## 9.4 4.6 5.6 2.8 2.7 1.6 2.3
##
## general/max 1.68 max/min = 3.6
## mean percent general = 0.32 with sd = 0.22 and cv of 0.68
## Explained Common Variance of the general factor = 0.32
##
## The degrees of freedom are 1705 and the fit is 8.03
## The number of observations was 495 with Chi Square = 3753.55 with prob < 2.4e-155
## The root mean square of the residuals is 0.04
## The df corrected root mean square of the residuals is 0.04
## RMSEA index = 0.049 and the 10 % confidence intervals are 0.047 0.051
## BIC = -6825.22
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 2015 and the fit is 22.88
## The number of observations was 495 with Chi Square = 10774.78 with prob < 0
## The root mean square of the residuals is 0.15
## The df corrected root mean square of the residuals is 0.15
##
## RMSEA index = 0.094 and the 10 % confidence intervals are 0.092 0.096
## BIC = -1727.41
##
## Measures of factor score adequacy
## g F1* F2* F3* F4* F5*
## Correlation of scores with factors 0.81 0.89 0.97 0.86 0.94 0.71
## Multiple R square of scores with factors 0.66 0.79 0.94 0.74 0.88 0.50
## Minimum correlation of factor score estimates 0.32 0.59 0.88 0.47 0.76 0.00
## F6*
## Correlation of scores with factors 0.91
## Multiple R square of scores with factors 0.83
## Minimum correlation of factor score estimates 0.66
##
## Total, General and Subset omega for each subset
## g F1* F2* F3* F4* F5*
## Omega total for total scores and subscales 0.96 0.87 0.91 0.86 0.83 0.81
## Omega general for total scores and subscales 0.60 0.43 0.11 0.45 0.09 0.57
## Omega group for total scores and subscales 0.21 0.44 0.79 0.41 0.74 0.24
## F6*
## Omega total for total scores and subscales 0.51
## Omega general for total scores and subscales 0.14
## Omega group for total scores and subscales 0.37splitHalf(taia %>% ungroup() %>% select(all_of(taia_items)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")## Split half reliabilities
## Call: splitHalf(r = taia %>% ungroup() %>% select(all_of(taia_items)),
## raw = F, brute = F, n.sample = 100, covar = F, check.keys = F,
## key = NULL, use = "pairwise")
##
## Maximum split half reliability (lambda 4) = 0.95
## Guttman lambda 6 = 0.96
## Average split half reliability = 0.93
## Guttman lambda 3 (alpha) = 0.93
## Guttman lambda 2 = 0.94
## Minimum split half reliability (beta) = 0.87
## Average interitem r = 0.17 with median = 0.18## Warning: Guttman has been deprecated. The use of the splitHalf function is
## recommended## Warning in splitHalf(r): Some items were negatively correlated with total scale
## and were automatically reversed.## Call: guttman(r = taia %>% ungroup() %>% select(all_of(taia_items)))
##
## Alternative estimates of reliability
##
## Guttman bounds
## L1 = 0.92
## L2 = 0.94
## L3 (alpha) = 0.93
## L4 (max) = 0.97
## L5 = 0.92
## L6 (smc) = 0.96
## TenBerge bounds
## mu0 = 0.93 mu1 = 0.94 mu2 = 0.94 mu3 = 0.94
##
## alpha of first PC = 0.95
## estimated greatest lower bound based upon communalities= 0.97
##
## beta found by splitHalf = 0.85## $glb
## [1] 0.965817
##
## $communality
## pr01 pr02 pr03 pr04 pr05 pr06 pr07 pr08
## 0.7253391 0.5393523 0.4690491 0.6292470 0.7830601 0.6280428 0.5758103 0.6112839
## pr09 pr10 co01 co02 co03 co04 co05 co06
## 0.5896570 0.3330770 0.5758243 0.6305638 0.4895706 0.4890165 0.7521395 0.5726406
## co07 co08 co09 co10 ut01 ut02 ut03 ut04
## 0.4356987 0.4665793 0.6337356 0.4811587 0.7718201 0.7277348 0.6422830 0.3722353
## ut05 ut06 ut07 ut08 ut09 ut10 ut11 ut12
## 0.5234489 0.7171434 0.5757501 0.5792722 0.7253062 0.3644927 0.6451266 0.5389414
## fa01 fa02 fa03 fa04 fa05 fa06 fa07 fa08
## 0.6943597 0.5928412 0.4376811 0.5197275 0.7565165 0.5844505 0.4786126 0.6061162
## fa09 fa10 de01 de02 de03 de04 de05 de06
## 0.6945327 0.4919291 0.4216417 0.5711187 0.5654506 0.4521109 0.7942688 0.7264322
## de07 de08 de09 de10 de11 un01 un02 un03
## 0.5278337 0.5885239 0.6875419 0.6514868 0.3425354 0.6718202 0.7379031 0.4402195
## un04 un05 un06 un07 un08 un09 un10 un11
## 0.6398019 0.6914535 0.4729219 0.5759967 0.6904648 0.5762576 0.6507091 0.6870705
## un12
## 0.6276157
##
## $numf
## [1] 19
##
## $Call
## glb.fa(r = taia %>% ungroup() %>% select(all_of(taia_items)))Exploratory Factor Analysis
6 factors, varimax rotation
efa_6f_vm <- factanal(taia %>% select(all_of(taia_items)),
factors = 6,
scores = "regression",
rotation = "varimax")##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## pr01 0.489 0.475 0.172 0.287
## pr02 0.263 0.392 0.219 0.164 0.181
## pr03 0.219 0.547
## pr04 -0.109 0.139 0.437
## pr05 0.204 0.350 0.139 0.123 0.676
## pr06 0.475 0.279 0.144 0.105
## pr07 0.387 0.525 0.208 0.134
## pr08 0.492 0.429 0.125 0.241
## pr09 0.350 0.347 0.133 0.220
## pr10 0.328 0.301 0.124 0.108
## co01 0.243 0.676 0.116
## co02 0.169 0.626 0.115
## co03 0.416 0.370 0.159 0.141
## co04 0.532 0.147 0.121 0.161
## co05 0.137 0.724
## co06 0.152 0.554 -0.117
## co08 -0.118 0.413 -0.115 -0.127 -0.467
## co09 0.676 -0.160
## co10 0.184 0.526 0.206 -0.123
## ut01 0.810
## ut02 0.806 0.102 0.135 0.125
## ut03 0.472 -0.102 0.119 0.507
## ut04 0.461 0.104 0.132
## ut05 0.598 0.194
## ut06 0.717 0.164 0.125
## ut07 0.550 0.207
## ut08 0.576 0.249
## ut09 0.593 0.174 0.102 0.128
## ut11 0.368 0.255 0.158 0.126 0.591
## ut12 0.604 0.233 0.135 0.142
## fa01 0.300 0.345 0.628
## fa02 -0.207 0.669 0.148
## fa03 -0.116 0.385 -0.318 0.148
## fa04 0.549 0.103 -0.192 0.177
## fa05 0.293 0.411 0.622
## fa06 0.301 0.573 0.126 0.188 0.182 0.163
## fa07 0.196 0.474 0.193
## fa08 -0.191 0.602 0.232
## fa09 -0.155 0.631 0.310
## fa10 0.280 0.136 0.599
## de01 0.278 0.429 0.162
## de02 0.225 0.571 0.129 0.155 0.228
## de03 0.223 0.475 0.115 0.138 0.214
## de05 0.508 0.173 0.137 0.385
## de06 0.218 0.352 0.184 0.171 0.666
## de07 0.439 0.301 0.220 0.182
## de08 0.345 0.412 0.193 0.107 0.185 0.205
## de09 0.259 0.592
## de10 0.287 0.512 0.177 0.351
## de11 0.181 0.332 0.255
## un01 0.287 0.731
## un02 0.138 0.815
## un03 0.207 0.502 -0.247
## un04 0.113 0.711
## un05 0.191 0.791
## un06 -0.131 0.518 0.189
## un07 0.286 0.648 -0.139 0.128
## un08 0.182 0.746
## un09 0.162 0.662 0.197
## un10 0.261 0.690
## un11 0.763
## un12 0.236 0.713
##
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## SS loadings 7.605 7.077 6.534 2.832 2.786 2.029
## Proportion Var 0.123 0.114 0.105 0.046 0.045 0.033
## Cumulative Var 0.123 0.237 0.342 0.388 0.433 0.466| U | |
|---|---|
| de11 | 0.7817421 |
| pr04 | 0.7700534 |
| pr10 | 0.7616189 |
| ut04 | 0.7559106 |
| fa03 | 0.7134353 |
| de01 | 0.7002496 |
| fa07 | 0.6907293 |
| pr09 | 0.6888357 |
| un06 | 0.6751271 |
| pr02 | 0.6694849 |
| pr06 | 0.6576002 |
| ut07 | 0.6510578 |
| co04 | 0.6481729 |
| co06 | 0.6474121 |
| de03 | 0.6464066 |
| un03 | 0.6402866 |
| pr03 | 0.6384384 |
| co03 | 0.6327467 |
| co10 | 0.6282188 |
| de07 | 0.6246089 |
| fa04 | 0.6137315 |
| ut08 | 0.5910384 |
| ut05 | 0.5868681 |
| ut09 | 0.5865585 |
| de08 | 0.5858475 |
| de09 | 0.5766570 |
| co08 | 0.5671255 |
| co02 | 0.5579891 |
| de05 | 0.5437340 |
| fa10 | 0.5420869 |
| ut12 | 0.5382611 |
| fa08 | 0.5284220 |
| de02 | 0.5246474 |
| co09 | 0.5063667 |
| pr07 | 0.4996945 |
| pr08 | 0.4983024 |
| ut03 | 0.4937529 |
| de10 | 0.4934491 |
| un09 | 0.4875879 |
| fa09 | 0.4737183 |
| fa02 | 0.4731288 |
| fa06 | 0.4702934 |
| un04 | 0.4642368 |
| co01 | 0.4635405 |
| un07 | 0.4574466 |
| co05 | 0.4479825 |
| ut06 | 0.4362455 |
| un10 | 0.4358851 |
| un12 | 0.4312571 |
| pr01 | 0.4144342 |
| ut11 | 0.4071750 |
| un08 | 0.4066783 |
| un11 | 0.3931924 |
| fa01 | 0.3816574 |
| un01 | 0.3730108 |
| fa05 | 0.3486393 |
| pr05 | 0.3357658 |
| ut01 | 0.3321338 |
| un05 | 0.3254634 |
| de06 | 0.3122163 |
| un02 | 0.3085120 |
| ut02 | 0.3006345 |
6 factors, promax rotation
efa_6f_pm <- factanal(taia %>% select(all_of(taia_items)),
factors = 6,
scores = "regression",
rotation = "promax")##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## pr01 0.461 0.225 0.283
## pr02 0.338 0.110 0.148 0.136
## pr03 -0.110 0.614
## pr04 -0.141 0.484
## pr05 0.790
## pr06 0.174 0.410
## pr07 0.527 0.131 0.192
## pr08 0.422 0.278 0.238
## pr09 0.361 0.146 0.227
## pr10 0.263 0.204 -0.122
## co01 0.809 0.102 -0.145
## co02 0.765 -0.137
## co03 0.303 0.264 0.114
## co04 0.493 0.121 -0.132
## co05 0.902 -0.102 -0.117 -0.109
## co06 0.668 -0.154
## co08 0.542 -0.180 -0.501 -0.100
## co09 0.817 -0.113 -0.123 -0.169
## co10 0.598 0.115 -0.135
## ut01 -0.249 0.974
## ut02 -0.231 0.910 0.113
## ut03 -0.213 0.385 0.515
## ut04 -0.198 0.552 0.146
## ut05 0.658 -0.124
## ut06 0.789 0.108
## ut07 0.128 0.593
## ut08 0.186 0.543 -0.108
## ut09 0.608 0.110
## ut11 0.291 0.694
## ut12 0.106 0.559
## fa01 0.277 0.125 0.171 0.650
## fa02 -0.129 0.695
## fa03 0.418 -0.154 -0.379 0.158
## fa04 0.609 -0.225 -0.241 0.146
## fa05 0.367 0.105 0.156 -0.119 0.648
## fa06 0.580 0.149 0.131
## fa07 -0.218 0.508 0.109 0.173
## fa08 -0.179 0.165 0.604
## fa09 -0.187 0.254 0.627
## fa10 0.281 -0.127 0.648 -0.119
## de01 0.460
## de02 0.569 0.135 0.166
## de03 0.430 0.173
## de05 0.114 0.340 0.407 -0.107
## de06 0.770
## de07 0.160 0.109 0.337 0.156
## de08 0.329 0.129 0.141 0.153
## de09 0.692 -0.101
## de10 0.397 0.106 0.103 0.339
## de11 -0.106 -0.103 0.320 0.101 0.273
## un01 -0.148 0.804 0.225 0.123 -0.141
## un02 0.857 -0.105
## un03 0.451 0.145 -0.247
## un04 0.734
## un05 0.844
## un06 0.556 -0.347 0.256
## un07 0.192 0.641 -0.115 -0.172 0.113
## un08 -0.114 0.817 0.113
## un09 0.646 -0.101 0.212
## un10 0.219 0.698 -0.212
## un11 0.803 -0.103
## un12 -0.180 0.740 0.166
##
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## SS loadings 7.535 6.547 6.389 3.041 2.823 2.419
## Proportion Var 0.122 0.106 0.103 0.049 0.046 0.039
## Cumulative Var 0.122 0.227 0.330 0.379 0.425 0.464| U | |
|---|---|
| de11 | 0.7817421 |
| pr04 | 0.7700534 |
| pr10 | 0.7616189 |
| ut04 | 0.7559106 |
| fa03 | 0.7134353 |
| de01 | 0.7002496 |
| fa07 | 0.6907293 |
| pr09 | 0.6888357 |
| un06 | 0.6751271 |
| pr02 | 0.6694849 |
| pr06 | 0.6576002 |
| ut07 | 0.6510578 |
| co04 | 0.6481729 |
| co06 | 0.6474121 |
| de03 | 0.6464066 |
| un03 | 0.6402866 |
| pr03 | 0.6384384 |
| co03 | 0.6327467 |
| co10 | 0.6282188 |
| de07 | 0.6246089 |
| fa04 | 0.6137315 |
| ut08 | 0.5910384 |
| ut05 | 0.5868681 |
| ut09 | 0.5865585 |
| de08 | 0.5858475 |
| de09 | 0.5766570 |
| co08 | 0.5671255 |
| co02 | 0.5579891 |
| de05 | 0.5437340 |
| fa10 | 0.5420869 |
| ut12 | 0.5382611 |
| fa08 | 0.5284220 |
| de02 | 0.5246474 |
| co09 | 0.5063667 |
| pr07 | 0.4996945 |
| pr08 | 0.4983024 |
| ut03 | 0.4937529 |
| de10 | 0.4934491 |
| un09 | 0.4875879 |
| fa09 | 0.4737183 |
| fa02 | 0.4731288 |
| fa06 | 0.4702934 |
| un04 | 0.4642368 |
| co01 | 0.4635405 |
| un07 | 0.4574466 |
| co05 | 0.4479825 |
| ut06 | 0.4362455 |
| un10 | 0.4358851 |
| un12 | 0.4312571 |
| pr01 | 0.4144342 |
| ut11 | 0.4071750 |
| un08 | 0.4066783 |
| un11 | 0.3931924 |
| fa01 | 0.3816574 |
| un01 | 0.3730108 |
| fa05 | 0.3486393 |
| pr05 | 0.3357658 |
| ut01 | 0.3321338 |
| un05 | 0.3254634 |
| de06 | 0.3122163 |
| un02 | 0.3085120 |
| ut02 | 0.3006345 |
5 factors, varimax rotation
efa_5f_vm <- factanal(taia %>% select(all_of(taia_items)),
factors = 5,
scores = "regression",
rotation = "varimax")##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5
## pr01 0.576 0.372 0.170 0.212
## pr02 0.319 0.359 0.221 0.205
## pr03 0.306 -0.108 0.412 0.121
## pr04 0.131 -0.197 0.340 0.154
## pr05 0.211 0.453 0.156 0.482
## pr06 0.501 0.249 0.142 0.100
## pr07 0.456 0.465 0.232
## pr08 0.567 0.334 0.123 0.160
## pr09 0.424 0.257 0.129 0.129
## pr10 0.369 0.262 0.124 0.104
## co01 0.303 0.625 0.130
## co02 0.235 0.573 0.114 0.112
## co03 0.462 0.329 0.176
## co04 0.573 0.115
## co05 0.197 0.690
## co06 0.207 0.520
## co08 -0.157 0.505 -0.112 -0.326 -0.146
## co09 0.121 0.681 -0.100
## co10 0.219 0.513 0.206 -0.125
## ut01 0.788
## ut02 0.792 0.100 0.136
## ut03 0.531 -0.225 0.358 0.140
## ut04 0.437 0.115
## ut05 0.586 0.191
## ut06 0.713 0.148
## ut07 0.559 0.171
## ut08 0.612 0.179
## ut09 0.601 0.152 0.128
## ut11 0.362 0.341 0.171 0.431
## ut12 0.632 0.179 0.131 0.115
## fa01 0.317 0.338 0.123 0.618
## fa02 -0.206 0.202 0.659
## fa03 -0.148 0.484 -0.103
## fa04 0.616
## fa05 0.305 0.412 0.604
## fa06 0.366 0.528 0.127 0.241 0.179
## fa07 0.512 0.182
## fa08 -0.190 0.284 0.591
## fa09 -0.155 0.321 0.632
## fa10 0.278 0.126 0.611
## de01 0.320 0.386 0.159
## de02 0.282 0.553 0.132 0.281
## de03 0.246 0.493 0.117 0.176 0.105
## de05 0.583 0.133 0.230
## de06 0.219 0.462 0.197 0.470
## de07 0.462 0.290 0.220 0.182
## de08 0.392 0.386 0.195 0.273
## de09 0.367 0.374
## de10 0.300 0.558 0.303 0.121
## de11 0.445 0.153
## un01 0.301 0.727
## un02 0.124 0.815
## un03 0.221 0.501 -0.243
## un04 0.107 0.116 0.712
## un05 0.219 0.787
## un06 0.519 0.148
## un07 0.328 0.651
## un08 0.195 0.744
## un09 0.198 0.667 0.124 -0.110
## un10 0.272 0.692 -0.101
## un11 0.107 0.764
## un12 0.242 0.712
##
## Factor1 Factor2 Factor3 Factor4 Factor5
## SS loadings 8.695 6.882 6.542 2.836 2.744
## Proportion Var 0.140 0.111 0.106 0.046 0.044
## Cumulative Var 0.140 0.251 0.357 0.403 0.447| U | |
|---|---|
| pr04 | 0.8036037 |
| ut04 | 0.7875168 |
| de11 | 0.7660851 |
| pr10 | 0.7648954 |
| fa03 | 0.7322846 |
| pr09 | 0.7157810 |
| de09 | 0.7138430 |
| de01 | 0.7115636 |
| pr03 | 0.7071547 |
| un06 | 0.6984492 |
| fa07 | 0.6934404 |
| pr02 | 0.6780781 |
| co06 | 0.6691958 |
| pr06 | 0.6557484 |
| ut07 | 0.6500545 |
| co04 | 0.6435044 |
| de03 | 0.6411034 |
| un03 | 0.6404520 |
| co03 | 0.6336224 |
| co10 | 0.6291329 |
| de07 | 0.6207641 |
| ut05 | 0.6108805 |
| fa04 | 0.6096628 |
| ut09 | 0.5950296 |
| co02 | 0.5895055 |
| ut08 | 0.5880908 |
| de05 | 0.5859234 |
| co08 | 0.5800904 |
| de08 | 0.5776240 |
| ut12 | 0.5381209 |
| ut11 | 0.5378917 |
| fa10 | 0.5312032 |
| fa08 | 0.5256412 |
| pr08 | 0.5255447 |
| ut03 | 0.5181711 |
| de02 | 0.5158859 |
| co09 | 0.5114014 |
| pr07 | 0.5096638 |
| co01 | 0.4937745 |
| pr05 | 0.4926627 |
| de10 | 0.4869821 |
| un09 | 0.4861097 |
| fa06 | 0.4806029 |
| co05 | 0.4747549 |
| fa09 | 0.4734086 |
| fa02 | 0.4713133 |
| de06 | 0.4691654 |
| un04 | 0.4646410 |
| un07 | 0.4595727 |
| ut06 | 0.4594009 |
| pr01 | 0.4496805 |
| un10 | 0.4361787 |
| un12 | 0.4325425 |
| un08 | 0.4008023 |
| un11 | 0.3971241 |
| fa01 | 0.3854741 |
| ut01 | 0.3691461 |
| un01 | 0.3671937 |
| fa05 | 0.3613714 |
| ut02 | 0.3407660 |
| un05 | 0.3303704 |
| un02 | 0.3106689 |
5 factors, promax rotation
efa_5f_pm <- factanal(taia %>% select(all_of(taia_items)),
factors = 5,
scores = "regression",
rotation = "promax")##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5
## pr01 0.446 0.279 0.163
## pr02 0.143 0.296 0.114 0.211
## pr03 0.233 -0.214 -0.117 0.424
## pr04 -0.293 0.354 0.107
## pr05 -0.115 0.392 0.579
## pr06 0.456 0.170
## pr07 0.281 0.412 0.218
## pr08 0.480 0.250 0.106
## pr09 0.342 0.189
## pr10 0.300 0.206 -0.112
## co01 0.146 0.650 0.122
## co02 0.599 0.111
## co03 0.354 0.265 0.139
## co04 0.624
## co05 0.759 -0.112
## co06 0.545 -0.122
## co08 -0.181 0.655 -0.178 -0.313 -0.109
## co09 0.763 -0.105 -0.100
## co10 0.114 0.531 0.122 -0.157
## ut01 0.932 -0.173 -0.161
## ut02 0.859 -0.129
## ut03 0.556 -0.375 0.303
## ut04 0.452
## ut05 0.610 0.111
## ut06 0.757
## ut07 0.631 0.114 -0.191
## ut08 0.655 -0.102
## ut09 0.605
## ut11 0.121 0.247 0.486
## ut12 0.625
## fa01 0.152 0.310 0.125 0.634
## fa02 -0.203 0.143 0.681
## fa03 -0.291 0.581
## fa04 -0.281 0.695
## fa05 0.129 0.400 0.620
## fa06 0.134 0.490 0.229 0.130
## fa07 -0.104 0.583 0.102
## fa08 -0.210 0.254 0.592
## fa09 -0.158 0.280 0.627
## fa10 0.247 0.110 -0.175 0.648
## de01 0.200 0.356
## de02 0.524 0.319
## de03 0.478 0.183
## de05 0.562 0.179
## de06 -0.118 0.396 0.550
## de07 0.346 0.199 0.104 0.156
## de08 0.192 0.310 0.272
## de09 0.305 -0.213 0.381
## de10 0.534 0.332
## de11 -0.110 0.509
## un01 0.258 -0.197 0.810 -0.169 0.132
## un02 0.857
## un03 0.185 0.450 -0.246
## un04 0.739
## un05 0.103 -0.121 0.847
## un06 -0.250 -0.119 0.556 0.200
## un07 -0.175 0.266 0.641
## un08 0.101 -0.128 0.827 -0.111 0.126
## un09 -0.166 0.647 0.170
## un10 -0.202 0.204 0.699
## un11 0.805
## un12 0.151 -0.155 0.746
##
## Factor1 Factor2 Factor3 Factor4 Factor5
## SS loadings 7.635 6.936 6.600 3.174 2.713
## Proportion Var 0.123 0.112 0.106 0.051 0.044
## Cumulative Var 0.123 0.235 0.341 0.393 0.436| U | |
|---|---|
| pr04 | 0.8036037 |
| ut04 | 0.7875168 |
| de11 | 0.7660851 |
| pr10 | 0.7648954 |
| fa03 | 0.7322846 |
| pr09 | 0.7157810 |
| de09 | 0.7138430 |
| de01 | 0.7115636 |
| pr03 | 0.7071547 |
| un06 | 0.6984492 |
| fa07 | 0.6934404 |
| pr02 | 0.6780781 |
| co06 | 0.6691958 |
| pr06 | 0.6557484 |
| ut07 | 0.6500545 |
| co04 | 0.6435044 |
| de03 | 0.6411034 |
| un03 | 0.6404520 |
| co03 | 0.6336224 |
| co10 | 0.6291329 |
| de07 | 0.6207641 |
| ut05 | 0.6108805 |
| fa04 | 0.6096628 |
| ut09 | 0.5950296 |
| co02 | 0.5895055 |
| ut08 | 0.5880908 |
| de05 | 0.5859234 |
| co08 | 0.5800904 |
| de08 | 0.5776240 |
| ut12 | 0.5381209 |
| ut11 | 0.5378917 |
| fa10 | 0.5312032 |
| fa08 | 0.5256412 |
| pr08 | 0.5255447 |
| ut03 | 0.5181711 |
| de02 | 0.5158859 |
| co09 | 0.5114014 |
| pr07 | 0.5096638 |
| co01 | 0.4937745 |
| pr05 | 0.4926627 |
| de10 | 0.4869821 |
| un09 | 0.4861097 |
| fa06 | 0.4806029 |
| co05 | 0.4747549 |
| fa09 | 0.4734086 |
| fa02 | 0.4713133 |
| de06 | 0.4691654 |
| un04 | 0.4646410 |
| un07 | 0.4595727 |
| ut06 | 0.4594009 |
| pr01 | 0.4496805 |
| un10 | 0.4361787 |
| un12 | 0.4325425 |
| un08 | 0.4008023 |
| un11 | 0.3971241 |
| fa01 | 0.3854741 |
| ut01 | 0.3691461 |
| un01 | 0.3671937 |
| fa05 | 0.3613714 |
| ut02 | 0.3407660 |
| un05 | 0.3303704 |
| un02 | 0.3106689 |
Confirmatory Factor Analysis
Basic model
Model:
mdl1 <- "
PR =~ pr01 + pr02 + pr03 + pr04 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa03 + fa04 + fa05 + fa06 + fa07 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10 + de11
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
"CFA model fitting:
## lavaan 0.6-8 ended normally after 62 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 139
##
## Number of observations 495
##
## Model Test User Model:
##
## Test statistic 6326.223
## Degrees of freedom 1814
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PR =~
## pr01 1.000
## pr02 0.727 0.055 13.199 0.000
## pr03 0.386 0.060 6.384 0.000
## pr04 0.153 0.063 2.431 0.015
## pr05 0.870 0.068 12.721 0.000
## pr06 0.808 0.061 13.285 0.000
## pr07 1.040 0.061 17.095 0.000
## pr08 0.847 0.050 17.029 0.000
## pr09 0.714 0.054 13.167 0.000
## pr10 0.666 0.060 11.105 0.000
## CO =~
## co01 1.000
## co02 0.896 0.061 14.596 0.000
## co03 0.646 0.061 10.618 0.000
## co04 0.459 0.065 7.065 0.000
## co05 1.090 0.065 16.652 0.000
## co06 0.856 0.066 13.052 0.000
## co08 0.438 0.062 7.047 0.000
## co09 0.978 0.063 15.527 0.000
## co10 0.831 0.065 12.804 0.000
## UT =~
## ut01 1.000
## ut02 1.083 0.055 19.815 0.000
## ut03 0.682 0.062 11.048 0.000
## ut04 0.636 0.062 10.244 0.000
## ut05 0.965 0.066 14.724 0.000
## ut06 1.008 0.058 17.348 0.000
## ut07 0.790 0.062 12.676 0.000
## ut08 0.807 0.057 14.082 0.000
## ut09 0.945 0.063 14.930 0.000
## ut11 0.789 0.068 11.527 0.000
## ut12 0.974 0.062 15.791 0.000
## FA =~
## fa01 1.000
## fa02 0.535 0.060 8.869 0.000
## fa03 0.219 0.060 3.670 0.000
## fa04 0.424 0.056 7.575 0.000
## fa05 1.028 0.051 20.280 0.000
## fa06 0.716 0.053 13.558 0.000
## fa07 0.335 0.057 5.911 0.000
## fa08 0.487 0.058 8.366 0.000
## fa09 0.626 0.059 10.519 0.000
## fa10 0.785 0.059 13.357 0.000
## DE =~
## de01 1.000
## de02 1.315 0.113 11.688 0.000
## de03 1.183 0.111 10.668 0.000
## de05 0.981 0.103 9.509 0.000
## de06 1.276 0.116 11.019 0.000
## de07 0.979 0.093 10.586 0.000
## de08 1.185 0.102 11.579 0.000
## de09 0.604 0.098 6.189 0.000
## de10 1.377 0.116 11.853 0.000
## de11 0.476 0.096 4.958 0.000
## UN =~
## un01 1.000
## un02 1.215 0.064 18.860 0.000
## un03 0.817 0.068 11.959 0.000
## un04 1.025 0.062 16.485 0.000
## un05 1.142 0.063 18.233 0.000
## un06 0.783 0.072 10.830 0.000
## un07 1.040 0.068 15.336 0.000
## un08 1.120 0.066 16.897 0.000
## un09 1.090 0.071 15.404 0.000
## un10 1.050 0.066 15.883 0.000
## un11 1.192 0.069 17.382 0.000
## un12 1.065 0.064 16.524 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PR ~~
## CO 0.417 0.043 9.702 0.000
## UT 0.479 0.045 10.634 0.000
## FA 0.402 0.044 9.066 0.000
## DE 0.423 0.045 9.495 0.000
## UN 0.232 0.034 6.779 0.000
## CO ~~
## UT 0.280 0.038 7.332 0.000
## FA 0.355 0.044 8.018 0.000
## DE 0.326 0.039 8.350 0.000
## UN 0.171 0.033 5.125 0.000
## UT ~~
## FA 0.331 0.043 7.706 0.000
## DE 0.341 0.040 8.621 0.000
## UN 0.187 0.034 5.560 0.000
## FA ~~
## DE 0.366 0.043 8.534 0.000
## UN 0.061 0.035 1.738 0.082
## DE ~~
## UN 0.186 0.029 6.327 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .pr01 0.366 0.028 13.060 0.000
## .pr02 0.619 0.041 14.916 0.000
## .pr03 0.936 0.060 15.582 0.000
## .pr04 1.071 0.068 15.711 0.000
## .pr05 0.977 0.065 14.993 0.000
## .pr06 0.751 0.050 14.902 0.000
## .pr07 0.581 0.042 13.928 0.000
## .pr08 0.390 0.028 13.953 0.000
## .pr09 0.601 0.040 14.921 0.000
## .pr10 0.805 0.053 15.208 0.000
## .co01 0.526 0.040 13.135 0.000
## .co02 0.573 0.041 13.824 0.000
## .co03 0.780 0.052 15.010 0.000
## .co04 1.045 0.068 15.460 0.000
## .co05 0.481 0.039 12.355 0.000
## .co06 0.763 0.053 14.429 0.000
## .co08 0.957 0.062 15.462 0.000
## .co09 0.536 0.040 13.296 0.000
## .co10 0.763 0.053 14.505 0.000
## .ut01 0.443 0.033 13.514 0.000
## .ut02 0.333 0.027 12.266 0.000
## .ut03 0.918 0.060 15.239 0.000
## .ut04 0.959 0.063 15.322 0.000
## .ut05 0.844 0.058 14.654 0.000
## .ut06 0.528 0.038 13.844 0.000
## .ut07 0.866 0.058 15.029 0.000
## .ut08 0.674 0.046 14.788 0.000
## .ut09 0.776 0.053 14.606 0.000
## .ut11 1.107 0.073 15.183 0.000
## .ut12 0.689 0.048 14.384 0.000
## .fa01 0.393 0.036 11.016 0.000
## .fa02 1.156 0.076 15.296 0.000
## .fa03 1.245 0.079 15.664 0.000
## .fa04 1.028 0.067 15.424 0.000
## .fa05 0.348 0.034 10.153 0.000
## .fa06 0.744 0.051 14.506 0.000
## .fa07 1.092 0.070 15.551 0.000
## .fa08 1.094 0.071 15.349 0.000
## .fa09 1.071 0.071 15.086 0.000
## .fa10 0.932 0.064 14.555 0.000
## .de01 0.822 0.055 14.942 0.000
## .de02 0.678 0.048 14.054 0.000
## .de03 0.895 0.061 14.713 0.000
## .de05 0.985 0.065 15.098 0.000
## .de06 0.891 0.061 14.538 0.000
## .de07 0.636 0.043 14.749 0.000
## .de08 0.583 0.041 14.151 0.000
## .de09 1.297 0.083 15.551 0.000
## .de10 0.678 0.049 13.889 0.000
## .de11 1.361 0.087 15.625 0.000
## .un01 0.500 0.035 14.388 0.000
## .un02 0.399 0.030 13.239 0.000
## .un03 0.962 0.063 15.267 0.000
## .un04 0.541 0.037 14.425 0.000
## .un05 0.425 0.031 13.665 0.000
## .un06 1.147 0.075 15.374 0.000
## .un07 0.729 0.049 14.737 0.000
## .un08 0.583 0.041 14.285 0.000
## .un09 0.789 0.054 14.721 0.000
## .un10 0.655 0.045 14.601 0.000
## .un11 0.583 0.041 14.094 0.000
## .un12 0.578 0.040 14.413 0.000
## PR 0.605 0.059 10.207 0.000
## CO 0.633 0.069 9.144 0.000
## UT 0.659 0.066 9.942 0.000
## FA 0.816 0.077 10.599 0.000
## DE 0.377 0.058 6.490 0.000
## UN 0.604 0.064 9.418 0.000Fit measures:
kable(tibble(
`Model 1` = c(
"Chi-Squared",
"DF",
"p",
"GFI",
"AGFI",
"CFI",
"TLI",
"SRMR",
"RMSEA"
),
Value = round(fitmeasures(
model1,
c(
"chisq",
"df",
"pvalue",
"gfi",
"agfi",
"cfi",
"tli",
"srmr",
"rmsea"
)
), 4)
))| Model 1 | Value |
|---|---|
| Chi-Squared | 6326.2235 |
| DF | 1814.0000 |
| p | 0.0000 |
| GFI | 0.6381 |
| AGFI | 0.6104 |
| CFI | 0.7096 |
| TLI | 0.6973 |
| SRMR | 0.1010 |
| RMSEA | 0.0709 |
Standardized solution:
Loadings:
kable(
smodel1 %>%
filter(op == "=~"),
col.names = c(
"Factor",
"",
"Item",
"Loading",
"SE",
"z",
"p",
"CI lower bound",
"CI upper bound"
),
digits = 3
)| Factor | Item | Loading | SE | z | p | CI lower bound | CI upper bound | |
|---|---|---|---|---|---|---|---|---|
| PR | =~ | pr01 | 0.789 | 0.020 | 39.913 | 0.000 | 0.751 | 0.828 |
| PR | =~ | pr02 | 0.584 | 0.032 | 18.261 | 0.000 | 0.521 | 0.647 |
| PR | =~ | pr03 | 0.296 | 0.043 | 6.854 | 0.000 | 0.211 | 0.381 |
| PR | =~ | pr04 | 0.114 | 0.047 | 2.456 | 0.014 | 0.023 | 0.206 |
| PR | =~ | pr05 | 0.565 | 0.033 | 17.164 | 0.000 | 0.501 | 0.630 |
| PR | =~ | pr06 | 0.587 | 0.032 | 18.467 | 0.000 | 0.525 | 0.650 |
| PR | =~ | pr07 | 0.728 | 0.024 | 30.723 | 0.000 | 0.682 | 0.775 |
| PR | =~ | pr08 | 0.726 | 0.024 | 30.439 | 0.000 | 0.679 | 0.773 |
| PR | =~ | pr09 | 0.583 | 0.032 | 18.186 | 0.000 | 0.520 | 0.645 |
| PR | =~ | pr10 | 0.500 | 0.036 | 13.888 | 0.000 | 0.429 | 0.570 |
| CO | =~ | co01 | 0.739 | 0.024 | 30.666 | 0.000 | 0.692 | 0.786 |
| CO | =~ | co02 | 0.686 | 0.027 | 25.127 | 0.000 | 0.632 | 0.739 |
| CO | =~ | co03 | 0.503 | 0.037 | 13.698 | 0.000 | 0.431 | 0.575 |
| CO | =~ | co04 | 0.337 | 0.043 | 7.853 | 0.000 | 0.253 | 0.421 |
| CO | =~ | co05 | 0.781 | 0.022 | 36.274 | 0.000 | 0.739 | 0.823 |
| CO | =~ | co06 | 0.615 | 0.031 | 19.688 | 0.000 | 0.554 | 0.676 |
| CO | =~ | co08 | 0.336 | 0.043 | 7.829 | 0.000 | 0.252 | 0.420 |
| CO | =~ | co09 | 0.728 | 0.025 | 29.429 | 0.000 | 0.680 | 0.777 |
| CO | =~ | co10 | 0.604 | 0.032 | 18.958 | 0.000 | 0.541 | 0.666 |
| UT | =~ | ut01 | 0.773 | 0.021 | 37.597 | 0.000 | 0.733 | 0.814 |
| UT | =~ | ut02 | 0.836 | 0.016 | 51.142 | 0.000 | 0.804 | 0.868 |
| UT | =~ | ut03 | 0.500 | 0.036 | 13.954 | 0.000 | 0.430 | 0.570 |
| UT | =~ | ut04 | 0.466 | 0.037 | 12.493 | 0.000 | 0.393 | 0.539 |
| UT | =~ | ut05 | 0.649 | 0.028 | 22.945 | 0.000 | 0.593 | 0.704 |
| UT | =~ | ut06 | 0.748 | 0.022 | 33.612 | 0.000 | 0.704 | 0.791 |
| UT | =~ | ut07 | 0.568 | 0.033 | 17.378 | 0.000 | 0.504 | 0.632 |
| UT | =~ | ut08 | 0.624 | 0.030 | 21.010 | 0.000 | 0.566 | 0.682 |
| UT | =~ | ut09 | 0.657 | 0.028 | 23.611 | 0.000 | 0.602 | 0.711 |
| UT | =~ | ut11 | 0.520 | 0.035 | 14.890 | 0.000 | 0.452 | 0.589 |
| UT | =~ | ut12 | 0.690 | 0.026 | 26.662 | 0.000 | 0.639 | 0.741 |
| FA | =~ | fa01 | 0.822 | 0.019 | 42.299 | 0.000 | 0.784 | 0.860 |
| FA | =~ | fa02 | 0.410 | 0.040 | 10.123 | 0.000 | 0.330 | 0.489 |
| FA | =~ | fa03 | 0.175 | 0.047 | 3.752 | 0.000 | 0.083 | 0.266 |
| FA | =~ | fa04 | 0.353 | 0.042 | 8.337 | 0.000 | 0.270 | 0.436 |
| FA | =~ | fa05 | 0.844 | 0.018 | 46.444 | 0.000 | 0.809 | 0.880 |
| FA | =~ | fa06 | 0.600 | 0.032 | 18.680 | 0.000 | 0.537 | 0.663 |
| FA | =~ | fa07 | 0.279 | 0.044 | 6.264 | 0.000 | 0.191 | 0.366 |
| FA | =~ | fa08 | 0.388 | 0.041 | 9.407 | 0.000 | 0.307 | 0.469 |
| FA | =~ | fa09 | 0.479 | 0.038 | 12.693 | 0.000 | 0.405 | 0.553 |
| FA | =~ | fa10 | 0.592 | 0.033 | 18.218 | 0.000 | 0.528 | 0.656 |
| DE | =~ | de01 | 0.561 | 0.033 | 16.808 | 0.000 | 0.495 | 0.626 |
| DE | =~ | de02 | 0.700 | 0.026 | 27.265 | 0.000 | 0.650 | 0.751 |
| DE | =~ | de03 | 0.609 | 0.031 | 19.717 | 0.000 | 0.548 | 0.670 |
| DE | =~ | de05 | 0.519 | 0.035 | 14.677 | 0.000 | 0.450 | 0.588 |
| DE | =~ | de06 | 0.639 | 0.029 | 21.837 | 0.000 | 0.582 | 0.696 |
| DE | =~ | de07 | 0.602 | 0.031 | 19.273 | 0.000 | 0.541 | 0.663 |
| DE | =~ | de08 | 0.690 | 0.026 | 26.215 | 0.000 | 0.638 | 0.741 |
| DE | =~ | de09 | 0.310 | 0.043 | 7.191 | 0.000 | 0.225 | 0.394 |
| DE | =~ | de10 | 0.716 | 0.025 | 29.002 | 0.000 | 0.668 | 0.765 |
| DE | =~ | de11 | 0.243 | 0.045 | 5.436 | 0.000 | 0.155 | 0.331 |
| UN | =~ | un01 | 0.740 | 0.022 | 33.462 | 0.000 | 0.696 | 0.783 |
| UN | =~ | un02 | 0.831 | 0.016 | 52.491 | 0.000 | 0.800 | 0.862 |
| UN | =~ | un03 | 0.544 | 0.033 | 16.332 | 0.000 | 0.478 | 0.609 |
| UN | =~ | un04 | 0.735 | 0.022 | 32.798 | 0.000 | 0.691 | 0.779 |
| UN | =~ | un05 | 0.806 | 0.018 | 45.760 | 0.000 | 0.771 | 0.840 |
| UN | =~ | un06 | 0.494 | 0.036 | 13.895 | 0.000 | 0.425 | 0.564 |
| UN | =~ | un07 | 0.687 | 0.025 | 27.059 | 0.000 | 0.638 | 0.737 |
| UN | =~ | un08 | 0.752 | 0.021 | 35.289 | 0.000 | 0.710 | 0.794 |
| UN | =~ | un09 | 0.690 | 0.025 | 27.358 | 0.000 | 0.641 | 0.740 |
| UN | =~ | un10 | 0.710 | 0.024 | 29.592 | 0.000 | 0.663 | 0.757 |
| UN | =~ | un11 | 0.772 | 0.020 | 38.620 | 0.000 | 0.732 | 0.811 |
| UN | =~ | un12 | 0.737 | 0.022 | 33.023 | 0.000 | 0.693 | 0.780 |
Covariances:
kable(
smodel1 %>%
filter(op == "~~" & lhs != rhs),
col.names = c("Factor", "", "Factor", "Covariance", "SE", "z", "p", "CI lower bound", "CI upper bound"),
digits = 3
)| Factor | Factor | Covariance | SE | z | p | CI lower bound | CI upper bound | |
|---|---|---|---|---|---|---|---|---|
| PR | ~~ | CO | 0.674 | 0.032 | 21.123 | 0.000 | 0.611 | 0.736 |
| PR | ~~ | UT | 0.758 | 0.025 | 29.745 | 0.000 | 0.708 | 0.808 |
| PR | ~~ | FA | 0.572 | 0.037 | 15.251 | 0.000 | 0.498 | 0.645 |
| PR | ~~ | DE | 0.886 | 0.019 | 47.788 | 0.000 | 0.849 | 0.922 |
| PR | ~~ | UN | 0.383 | 0.043 | 8.811 | 0.000 | 0.298 | 0.469 |
| CO | ~~ | UT | 0.434 | 0.042 | 10.255 | 0.000 | 0.351 | 0.517 |
| CO | ~~ | FA | 0.493 | 0.041 | 12.022 | 0.000 | 0.413 | 0.574 |
| CO | ~~ | DE | 0.667 | 0.033 | 20.416 | 0.000 | 0.603 | 0.731 |
| CO | ~~ | UN | 0.276 | 0.047 | 5.929 | 0.000 | 0.185 | 0.367 |
| UT | ~~ | FA | 0.451 | 0.042 | 10.813 | 0.000 | 0.369 | 0.532 |
| UT | ~~ | DE | 0.684 | 0.030 | 22.435 | 0.000 | 0.624 | 0.744 |
| UT | ~~ | UN | 0.296 | 0.045 | 6.570 | 0.000 | 0.207 | 0.384 |
| FA | ~~ | DE | 0.659 | 0.033 | 19.845 | 0.000 | 0.594 | 0.724 |
| FA | ~~ | UN | 0.087 | 0.050 | 1.762 | 0.078 | -0.010 | 0.185 |
| DE | ~~ | UN | 0.389 | 0.044 | 8.920 | 0.000 | 0.304 | 0.475 |
Residuals:
kable(
smodel1 %>%
filter(op == "~~" & lhs == rhs)%>%
select(-(2:3)),
col.names = c("Item", "Residual", "SE", "z", "p", "CI lower bound", "CI upper bound"),
digits = 3
)| Item | Residual | SE | z | p | CI lower bound | CI upper bound |
|---|---|---|---|---|---|---|
| pr01 | 0.377 | 0.031 | 12.076 | 0 | 0.316 | 0.438 |
| pr02 | 0.659 | 0.037 | 17.653 | 0 | 0.586 | 0.732 |
| pr03 | 0.912 | 0.026 | 35.640 | 0 | 0.862 | 0.962 |
| pr04 | 0.987 | 0.011 | 92.749 | 0 | 0.966 | 1.008 |
| pr05 | 0.681 | 0.037 | 18.291 | 0 | 0.608 | 0.754 |
| pr06 | 0.655 | 0.037 | 17.542 | 0 | 0.582 | 0.728 |
| pr07 | 0.470 | 0.035 | 13.609 | 0 | 0.402 | 0.537 |
| pr08 | 0.473 | 0.035 | 13.667 | 0 | 0.405 | 0.541 |
| pr09 | 0.661 | 0.037 | 17.694 | 0 | 0.587 | 0.734 |
| pr10 | 0.750 | 0.036 | 20.839 | 0 | 0.680 | 0.821 |
| co01 | 0.454 | 0.036 | 12.730 | 0 | 0.384 | 0.523 |
| co02 | 0.530 | 0.037 | 14.153 | 0 | 0.456 | 0.603 |
| co03 | 0.747 | 0.037 | 20.238 | 0 | 0.675 | 0.820 |
| co04 | 0.887 | 0.029 | 30.728 | 0 | 0.830 | 0.943 |
| co05 | 0.390 | 0.034 | 11.593 | 0 | 0.324 | 0.456 |
| co06 | 0.622 | 0.038 | 16.181 | 0 | 0.546 | 0.697 |
| co08 | 0.887 | 0.029 | 30.806 | 0 | 0.831 | 0.944 |
| co09 | 0.469 | 0.036 | 13.014 | 0 | 0.399 | 0.540 |
| co10 | 0.636 | 0.038 | 16.536 | 0 | 0.560 | 0.711 |
| ut01 | 0.402 | 0.032 | 12.630 | 0 | 0.340 | 0.464 |
| ut02 | 0.301 | 0.027 | 11.006 | 0 | 0.247 | 0.354 |
| ut03 | 0.750 | 0.036 | 20.907 | 0 | 0.679 | 0.820 |
| ut04 | 0.783 | 0.035 | 22.500 | 0 | 0.715 | 0.851 |
| ut05 | 0.579 | 0.037 | 15.774 | 0 | 0.507 | 0.651 |
| ut06 | 0.441 | 0.033 | 13.251 | 0 | 0.376 | 0.506 |
| ut07 | 0.678 | 0.037 | 18.290 | 0 | 0.605 | 0.751 |
| ut08 | 0.611 | 0.037 | 16.492 | 0 | 0.538 | 0.683 |
| ut09 | 0.568 | 0.037 | 15.554 | 0 | 0.497 | 0.640 |
| ut11 | 0.729 | 0.036 | 20.063 | 0 | 0.658 | 0.801 |
| ut12 | 0.524 | 0.036 | 14.683 | 0 | 0.454 | 0.594 |
| fa01 | 0.325 | 0.032 | 10.177 | 0 | 0.262 | 0.387 |
| fa02 | 0.832 | 0.033 | 25.070 | 0 | 0.767 | 0.897 |
| fa03 | 0.969 | 0.016 | 59.581 | 0 | 0.938 | 1.001 |
| fa04 | 0.875 | 0.030 | 29.210 | 0 | 0.816 | 0.934 |
| fa05 | 0.287 | 0.031 | 9.357 | 0 | 0.227 | 0.347 |
| fa06 | 0.640 | 0.039 | 16.624 | 0 | 0.565 | 0.716 |
| fa07 | 0.922 | 0.025 | 37.218 | 0 | 0.874 | 0.971 |
| fa08 | 0.849 | 0.032 | 26.527 | 0 | 0.787 | 0.912 |
| fa09 | 0.770 | 0.036 | 21.268 | 0 | 0.699 | 0.841 |
| fa10 | 0.649 | 0.038 | 16.872 | 0 | 0.574 | 0.725 |
| de01 | 0.686 | 0.037 | 18.318 | 0 | 0.612 | 0.759 |
| de02 | 0.510 | 0.036 | 14.162 | 0 | 0.439 | 0.580 |
| de03 | 0.629 | 0.038 | 16.722 | 0 | 0.555 | 0.703 |
| de05 | 0.731 | 0.037 | 19.910 | 0 | 0.659 | 0.803 |
| de06 | 0.592 | 0.037 | 15.832 | 0 | 0.519 | 0.665 |
| de07 | 0.637 | 0.038 | 16.934 | 0 | 0.564 | 0.711 |
| de08 | 0.524 | 0.036 | 14.436 | 0 | 0.453 | 0.595 |
| de09 | 0.904 | 0.027 | 33.919 | 0 | 0.852 | 0.956 |
| de10 | 0.487 | 0.035 | 13.746 | 0 | 0.417 | 0.556 |
| de11 | 0.941 | 0.022 | 43.330 | 0 | 0.898 | 0.984 |
| un01 | 0.453 | 0.033 | 13.854 | 0 | 0.389 | 0.517 |
| un02 | 0.309 | 0.026 | 11.747 | 0 | 0.258 | 0.361 |
| un03 | 0.705 | 0.036 | 19.471 | 0 | 0.634 | 0.775 |
| un04 | 0.460 | 0.033 | 13.962 | 0 | 0.395 | 0.524 |
| un05 | 0.350 | 0.028 | 12.343 | 0 | 0.295 | 0.406 |
| un06 | 0.756 | 0.035 | 21.495 | 0 | 0.687 | 0.825 |
| un07 | 0.528 | 0.035 | 15.106 | 0 | 0.459 | 0.596 |
| un08 | 0.435 | 0.032 | 13.574 | 0 | 0.372 | 0.498 |
| un09 | 0.524 | 0.035 | 15.035 | 0 | 0.455 | 0.592 |
| un10 | 0.496 | 0.034 | 14.549 | 0 | 0.429 | 0.563 |
| un11 | 0.405 | 0.031 | 13.125 | 0 | 0.344 | 0.465 |
| un12 | 0.458 | 0.033 | 13.925 | 0 | 0.393 | 0.522 |
| PR | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
| CO | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
| UT | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
| FA | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
| DE | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
| UN | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
Visualization:
Model with general factor
Model:
mdl2 <- "
PR =~ pr01 + pr02 + pr03 + pr04 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa03 + fa04 + fa05 + fa06 + fa07 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10 + de11
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
DigTrust =~ PR + CO + UT + FA + DE + UN
"## lavaan 0.6-8 ended normally after 44 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 130
##
## Number of observations 495
##
## Model Test User Model:
##
## Test statistic 6381.193
## Degrees of freedom 1823
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PR =~
## pr01 1.000
## pr02 0.732 0.055 13.227 0.000
## pr03 0.389 0.061 6.409 0.000
## pr04 0.160 0.063 2.533 0.011
## pr05 0.882 0.069 12.845 0.000
## pr06 0.797 0.061 13.006 0.000
## pr07 1.049 0.061 17.137 0.000
## pr08 0.846 0.050 16.877 0.000
## pr09 0.719 0.055 13.186 0.000
## pr10 0.661 0.060 10.962 0.000
## CO =~
## co01 1.000
## co02 0.894 0.062 14.491 0.000
## co03 0.652 0.061 10.699 0.000
## co04 0.473 0.065 7.263 0.000
## co05 1.088 0.066 16.548 0.000
## co06 0.862 0.066 13.089 0.000
## co08 0.435 0.062 6.976 0.000
## co09 0.977 0.063 15.442 0.000
## co10 0.834 0.065 12.798 0.000
## UT =~
## ut01 1.000
## ut02 1.080 0.055 19.757 0.000
## ut03 0.674 0.062 10.910 0.000
## ut04 0.635 0.062 10.237 0.000
## ut05 0.972 0.065 14.837 0.000
## ut06 1.007 0.058 17.336 0.000
## ut07 0.791 0.062 12.699 0.000
## ut08 0.807 0.057 14.089 0.000
## ut09 0.946 0.063 14.941 0.000
## ut11 0.790 0.068 11.534 0.000
## ut12 0.973 0.062 15.774 0.000
## FA =~
## fa01 1.000
## fa02 0.522 0.060 8.760 0.000
## fa03 0.204 0.059 3.452 0.001
## fa04 0.407 0.055 7.352 0.000
## fa05 1.017 0.050 20.447 0.000
## fa06 0.704 0.052 13.544 0.000
## fa07 0.323 0.056 5.749 0.000
## fa08 0.475 0.058 8.239 0.000
## fa09 0.616 0.059 10.488 0.000
## fa10 0.780 0.058 13.496 0.000
## DE =~
## de01 1.000
## de02 1.314 0.113 11.628 0.000
## de03 1.176 0.111 10.576 0.000
## de05 1.006 0.104 9.645 0.000
## de06 1.267 0.116 10.914 0.000
## de07 0.989 0.093 10.609 0.000
## de08 1.188 0.103 11.539 0.000
## de09 0.623 0.098 6.342 0.000
## de10 1.371 0.117 11.764 0.000
## de11 0.475 0.096 4.934 0.000
## UN =~
## un01 1.000
## un02 1.212 0.064 18.935 0.000
## un03 0.811 0.068 11.917 0.000
## un04 1.022 0.062 16.530 0.000
## un05 1.140 0.062 18.326 0.000
## un06 0.780 0.072 10.831 0.000
## un07 1.035 0.067 15.346 0.000
## un08 1.118 0.066 16.974 0.000
## un09 1.085 0.070 15.412 0.000
## un10 1.046 0.066 15.902 0.000
## un11 1.188 0.068 17.430 0.000
## un12 1.061 0.064 16.562 0.000
## DigTrust =~
## PR 1.000
## CO 0.746 0.061 12.327 0.000
## UT 0.818 0.060 13.693 0.000
## FA 0.781 0.064 12.133 0.000
## DE 0.777 0.066 11.697 0.000
## UN 0.408 0.053 7.651 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .pr01 0.369 0.028 13.031 0.000
## .pr02 0.616 0.041 14.883 0.000
## .pr03 0.935 0.060 15.576 0.000
## .pr04 1.070 0.068 15.709 0.000
## .pr05 0.966 0.065 14.947 0.000
## .pr06 0.763 0.051 14.921 0.000
## .pr07 0.573 0.041 13.836 0.000
## .pr08 0.393 0.028 13.938 0.000
## .pr09 0.599 0.040 14.890 0.000
## .pr10 0.810 0.053 15.208 0.000
## .co01 0.528 0.040 13.119 0.000
## .co02 0.578 0.042 13.832 0.000
## .co03 0.775 0.052 14.982 0.000
## .co04 1.037 0.067 15.438 0.000
## .co05 0.485 0.039 12.362 0.000
## .co06 0.759 0.053 14.391 0.000
## .co08 0.959 0.062 15.463 0.000
## .co09 0.539 0.041 13.290 0.000
## .co10 0.761 0.053 14.482 0.000
## .ut01 0.442 0.033 13.486 0.000
## .ut02 0.336 0.027 12.276 0.000
## .ut03 0.925 0.061 15.248 0.000
## .ut04 0.959 0.063 15.318 0.000
## .ut05 0.836 0.057 14.615 0.000
## .ut06 0.528 0.038 13.826 0.000
## .ut07 0.864 0.058 15.017 0.000
## .ut08 0.673 0.046 14.776 0.000
## .ut09 0.775 0.053 14.590 0.000
## .ut11 1.106 0.073 15.176 0.000
## .ut12 0.690 0.048 14.373 0.000
## .fa01 0.375 0.035 10.619 0.000
## .fa02 1.162 0.076 15.307 0.000
## .fa03 1.250 0.080 15.672 0.000
## .fa04 1.036 0.067 15.443 0.000
## .fa05 0.348 0.035 10.076 0.000
## .fa06 0.748 0.052 14.517 0.000
## .fa07 1.097 0.070 15.560 0.000
## .fa08 1.100 0.072 15.361 0.000
## .fa09 1.074 0.071 15.090 0.000
## .fa10 0.927 0.064 14.529 0.000
## .de01 0.823 0.055 14.925 0.000
## .de02 0.681 0.049 14.028 0.000
## .de03 0.904 0.061 14.713 0.000
## .de05 0.968 0.064 15.039 0.000
## .de06 0.902 0.062 14.545 0.000
## .de07 0.630 0.043 14.698 0.000
## .de08 0.583 0.041 14.109 0.000
## .de09 1.288 0.083 15.533 0.000
## .de10 0.687 0.049 13.891 0.000
## .de11 1.361 0.087 15.623 0.000
## .un01 0.497 0.035 14.367 0.000
## .un02 0.399 0.030 13.228 0.000
## .un03 0.966 0.063 15.272 0.000
## .un04 0.541 0.038 14.422 0.000
## .un05 0.423 0.031 13.641 0.000
## .un06 1.148 0.075 15.374 0.000
## .un07 0.732 0.050 14.740 0.000
## .un08 0.581 0.041 14.271 0.000
## .un09 0.792 0.054 14.725 0.000
## .un10 0.657 0.045 14.604 0.000
## .un11 0.584 0.041 14.091 0.000
## .un12 0.578 0.040 14.412 0.000
## .PR 0.051 0.017 2.954 0.003
## .CO 0.324 0.039 8.231 0.000
## .UT 0.290 0.033 8.734 0.000
## .FA 0.498 0.051 9.744 0.000
## .DE 0.043 0.012 3.438 0.001
## .UN 0.516 0.055 9.336 0.000
## DigTrust 0.552 0.058 9.531 0.000kable(tibble(
`Model 2` = c(
"Chi-Squared",
"DF",
"p",
"GFI",
"AGFI",
"CFI",
"TLI",
"SRMR",
"RMSEA"
),
Value = round(fitmeasures(
model2,
c(
"chisq",
"df",
"pvalue",
"gfi",
"agfi",
"cfi",
"tli",
"srmr",
"rmsea"
)
), 4)
))| Model 2 | Value |
|---|---|
| Chi-Squared | 6381.1932 |
| DF | 1823.0000 |
| p | 0.0000 |
| GFI | 0.6329 |
| AGFI | 0.6068 |
| CFI | 0.7067 |
| TLI | 0.6957 |
| SRMR | 0.1029 |
| RMSEA | 0.0711 |
Validation
TAIA total score
taia %>%
select(id, all_of(taia_items)) %>%
pivot_longer(all_of(taia_items),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = str_remove_all(subscale, "[:digit:]{2}") %>% toupper()) %>%
group_by(id, subscale) %>%
summarise(total_score = sum(score)) %>%
pivot_wider(id_cols = id,
names_from = subscale,
values_from = total_score) %>%
full_join(taia) -> taia## `summarise()` regrouping output by 'id' (override with `.groups` argument)## Joining, by = "id"Correlations with General Trust Scale
taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) -> taia_ltaia_l %>%
ggplot(aes(score, gt_score, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
scale_color_manual(values = clrs) +
guides(color = FALSE) +
labs(x = "TAIA subscale total score",
y = "General Trust Scale total score",
title = "Corelations between General Trust and TAIA subscales") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$gt_score
## t = 3.1872, df = 493, p-value = 0.001528
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05463758 0.22737163
## sample estimates:
## cor
## 0.1420861##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$gt_score
## t = 3.842, df = 493, p-value = 0.000138
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.08362567 0.25480562
## sample estimates:
## cor
## 0.1705018##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$gt_score
## t = 2.4679, df = 493, p-value = 0.01393
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02255584 0.19668679
## sample estimates:
## cor
## 0.110469##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$gt_score
## t = 2.0938, df = 493, p-value = 0.03678
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.005800447 0.180524218
## sample estimates:
## cor
## 0.0938852##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$gt_score
## t = 4.1369, df = 493, p-value = 4.139e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.09659152 0.26698780
## sample estimates:
## cor
## 0.183165##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$gt_score
## t = 3.5643, df = 493, p-value = 0.0004003
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07136383 0.24323469
## sample estimates:
## cor
## 0.1584997Correlations with questions
taia_l %>%
ggplot(aes(score, n_dighelp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Number of digital helpers",
title = "Correlation TAIA subscales with number of digital helpers") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 948 rows containing non-finite values (stat_smooth).## Warning: Removed 948 rows containing missing values (geom_point).
## Warning in cor.test.default(taia$PR, taia$n_dighelp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$PR and taia$n_dighelp
## S = 5561417, p-value = 0.01861
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1281319## Warning in cor.test.default(taia$CO, taia$n_dighelp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$CO and taia$n_dighelp
## S = 6618523, p-value = 0.4916
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.03759163## Warning in cor.test.default(taia$UT, taia$n_dighelp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$UT and taia$n_dighelp
## S = 5620719, p-value = 0.02917
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.118835## Warning in cor.test.default(taia$FA, taia$n_dighelp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$FA and taia$n_dighelp
## S = 6614475, p-value = 0.4989
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.03695705## Warning in cor.test.default(taia$DE, taia$n_dighelp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$DE and taia$n_dighelp
## S = 5705718, p-value = 0.05298
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1055096## Warning in cor.test.default(taia$UN, taia$n_dighelp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$UN and taia$n_dighelp
## S = 5912050, p-value = 0.1803
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.0731627taia_l %>%
ggplot(aes(score, e_dighelp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of dealing with digital helpers experience",
title = "Correlation TAIA subscales with expirience of dealing with digital helpers") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 948 rows containing non-finite values (stat_smooth).## Warning: Removed 948 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$e_dighelp
## t = 6.7852, df = 335, p-value = 5.27e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2500476 0.4381609
## sample estimates:
## cor
## 0.3475971##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$e_dighelp
## t = 4.0996, df = 335, p-value = 5.199e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1144032 0.3179773
## sample estimates:
## cor
## 0.218567##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$e_dighelp
## t = 5.5088, df = 335, p-value = 7.21e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1871349 0.3832429
## sample estimates:
## cor
## 0.288208##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$e_dighelp
## t = 4.1342, df = 335, p-value = 4.507e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1162249 0.3196358
## sample estimates:
## cor
## 0.2203243##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$e_dighelp
## t = 5.7293, df = 335, p-value = 2.248e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1982204 0.3930214
## sample estimates:
## cor
## 0.2987294##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$e_dighelp
## t = 1.7091, df = 335, p-value = 0.08836
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.01400204 0.19784231
## sample estimates:
## cor
## 0.09297223taia_l %>%
ggplot(aes(score, n_socnet, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Number of social networks and social media",
title = "Correlation TAIA subscales with number of social networks and social media") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 360 rows containing non-finite values (stat_smooth).## Warning: Removed 360 rows containing missing values (geom_point).
## Warning in cor.test.default(taia$PR, taia$n_socnet, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$PR and taia$n_socnet
## S = 12147347, p-value = 0.01685
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1145436## Warning in cor.test.default(taia$CO, taia$n_socnet, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$CO and taia$n_socnet
## S = 14058484, p-value = 0.6065
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.02476493## Warning in cor.test.default(taia$UT, taia$n_socnet, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$UT and taia$n_socnet
## S = 11573855, p-value = 0.001069
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1563471## Warning in cor.test.default(taia$FA, taia$n_socnet, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$FA and taia$n_socnet
## S = 12379302, p-value = 0.04181
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.09763564## Warning in cor.test.default(taia$DE, taia$n_socnet, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$DE and taia$n_socnet
## S = 11425218, p-value = 0.0004627
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1671817## Warning in cor.test.default(taia$UN, taia$n_socnet, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$UN and taia$n_socnet
## S = 12007801, p-value = 0.009219
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1247154taia_l %>%
ggplot(aes(score, f_socnet, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Frequency of social networks and social media use",
title = "Correlation TAIA subscales with frequency of social networks and social media use") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 360 rows containing non-finite values (stat_smooth).## Warning: Removed 360 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$f_socnet
## t = 1.4848, df = 433, p-value = 0.1383
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02299864 0.16409771
## sample estimates:
## cor
## 0.07117555##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$f_socnet
## t = 0.83383, df = 433, p-value = 0.4048
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.05418487 0.13355691
## sample estimates:
## cor
## 0.0400394##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$f_socnet
## t = -0.18344, df = 433, p-value = 0.8545
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.10275052 0.08527558
## sample estimates:
## cor
## -0.008815392##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$f_socnet
## t = 0.30431, df = 433, p-value = 0.761
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07950679 0.10849394
## sample estimates:
## cor
## 0.01462281##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$f_socnet
## t = 0.51348, df = 433, p-value = 0.6079
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06951288 0.11841428
## sample estimates:
## cor
## 0.02466864##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$f_socnet
## t = -0.29625, df = 433, p-value = 0.7672
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.10811106 0.07989175
## sample estimates:
## cor
## -0.01423547taia_l %>%
ggplot(aes(score, e_socnet, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of dealing with recommender systems experience",
title = "Correlation TAIA subscales with experience of dealing with recommender systems") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 360 rows containing non-finite values (stat_smooth).## Warning: Removed 360 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$e_socnet
## t = 4.6683, df = 433, p-value = 4.056e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1275089 0.3066146
## sample estimates:
## cor
## 0.2189049##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$e_socnet
## t = 5.3414, df = 433, p-value = 1.493e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1583093 0.3348224
## sample estimates:
## cor
## 0.2486289##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$e_socnet
## t = 4.0939, df = 433, p-value = 5.061e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1008503 0.2819433
## sample estimates:
## cor
## 0.1930402##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$e_socnet
## t = 2.5727, df = 433, p-value = 0.01042
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02901732 0.21425142
## sample estimates:
## cor
## 0.1227028##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$e_socnet
## t = 5.7293, df = 433, p-value = 1.89e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1758227 0.3507217
## sample estimates:
## cor
## 0.2654548##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$e_socnet
## t = 1.7866, df = 433, p-value = 0.0747
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.008545219 0.178131408
## sample estimates:
## cor
## 0.0855438Self driving cars and education AI
taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>%
ggplot(aes(score, selfdrexp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of selfdriving car experience",
title = "Correlation TAIA subscales with selfdriving car experience") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2892 rows containing non-finite values (stat_smooth).## Warning: Removed 2892 rows containing missing values (geom_point).
## Warning in cor.test.default(taia$PR, taia$selfdrexp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$PR and taia$selfdrexp
## S = 265.3, p-value = 0.3702
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2711565## Warning in cor.test.default(taia$CO, taia$selfdrexp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$CO and taia$selfdrexp
## S = 418.25, p-value = 0.627
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.1490473## Warning in cor.test.default(taia$UT, taia$selfdrexp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$UT and taia$selfdrexp
## S = 175.48, p-value = 0.06984
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.5179022## Warning in cor.test.default(taia$FA, taia$selfdrexp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$FA and taia$selfdrexp
## S = 272.56, p-value = 0.4077
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2512199## Warning in cor.test.default(taia$DE, taia$selfdrexp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$DE and taia$selfdrexp
## S = 326.24, p-value = 0.7359
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1037321## Warning in cor.test.default(taia$UN, taia$selfdrexp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$UN and taia$selfdrexp
## S = 358.61, p-value = 0.9617
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.01481886taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>%
ggplot(aes(score, selfdrsafe, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of selfdriving car safe",
title = "Correlation TAIA subscales with selfdriving car safe") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2892 rows containing non-finite values (stat_smooth).## Warning: Removed 2892 rows containing missing values (geom_point).
## Warning in cor.test.default(taia$PR, taia$selfdrsafe, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$PR and taia$selfdrsafe
## S = 372.22, p-value = 0.9417
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.02257133## Warning in cor.test.default(taia$CO, taia$selfdrsafe, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$CO and taia$selfdrsafe
## S = 323.96, p-value = 0.7205
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1099992## Warning in cor.test.default(taia$UT, taia$selfdrsafe, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$UT and taia$selfdrsafe
## S = 301.67, p-value = 0.576
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1712239## Warning in cor.test.default(taia$FA, taia$selfdrsafe, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$FA and taia$selfdrsafe
## S = 217.35, p-value = 0.1723
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.4028841## Warning in cor.test.default(taia$DE, taia$selfdrsafe, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$DE and taia$selfdrsafe
## S = 352.98, p-value = 0.9218
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.03026276## Warning in cor.test.default(taia$UN, taia$selfdrsafe, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$UN and taia$selfdrsafe
## S = 450.47, p-value = 0.4345
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.2375627taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>%
ggplot(aes(score, eduaiexp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of dealing with education AI experience",
title = "Correlation TAIA subscales with experience of dealing with education AI") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2298 rows containing non-finite values (stat_smooth).## Warning: Removed 2298 rows containing missing values (geom_point).
## Warning in cor.test.default(taia$PR, taia$eduaiexp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$PR and taia$eduaiexp
## S = 136920, p-value = 5.31e-06
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.4152137## Warning in cor.test.default(taia$CO, taia$eduaiexp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$CO and taia$eduaiexp
## S = 145289, p-value = 3.687e-05
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3794657## Warning in cor.test.default(taia$UT, taia$eduaiexp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$UT and taia$eduaiexp
## S = 142744, p-value = 2.094e-05
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3903391## Warning in cor.test.default(taia$FA, taia$eduaiexp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$FA and taia$eduaiexp
## S = 168570, p-value = 0.002786
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2800356## Warning in cor.test.default(taia$DE, taia$eduaiexp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$DE and taia$eduaiexp
## S = 112949, p-value = 5.1e-09
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.5175924## Warning in cor.test.default(taia$UN, taia$eduaiexp, method = "sp"): Cannot
## compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: taia$UN and taia$eduaiexp
## S = 159518, p-value = 0.0006156
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3186946